+publisher:"Texas A&M University" +contributor:("Mallick, Bani")

Texas A&M University

1. Prabhakar, Avinash. Uncertainty Propagation in Hypersonic Flight Dynamics and Comparison of Different Methods.

Degree: 2010, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-194

► In this work we present a novel computational framework for analyzing evolution of uncertainty in state trajectories of a hypersonic air vehicle due to uncertainty… (more)

Subjects/Keywords: Sample space sigma$-algebra Probability measure Non dimensional altitude Non dimensional velocity Flight path angle Lift over drag ratio Ballistic coefficient Gaussian Uniform

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APA (6^th Edition):

Prabhakar, A. (2010). Uncertainty Propagation in Hypersonic Flight Dynamics and Comparison of Different Methods. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-194

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Prabhakar, Avinash. “Uncertainty Propagation in Hypersonic Flight Dynamics and Comparison of Different Methods.” 2010. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-194.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Prabhakar, Avinash. “Uncertainty Propagation in Hypersonic Flight Dynamics and Comparison of Different Methods.” 2010. Web. 16 Oct 2019.

Vancouver:

Prabhakar A. Uncertainty Propagation in Hypersonic Flight Dynamics and Comparison of Different Methods. [Internet] [Thesis]. Texas A&M University; 2010. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-194.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Prabhakar A. Uncertainty Propagation in Hypersonic Flight Dynamics and Comparison of Different Methods. [Thesis]. Texas A&M University; 2010. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-194

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

2. Peterson, Jacob Ross. Exponentially-convergent Monte Carlo for the One-dimensional Transport Equation.

Degree: 2014, Texas A&M University

URL: http://hdl.handle.net/1969.1/152727

► An exponentially-convergent Monte Carlo (ECMC) method is analyzed using the one-group, one-dimension, slab-geometry transport equation. The method is based upon the use of a linear… (more)

Subjects/Keywords: Monte Carlo; Geometric Monte Carlo; Exponential Monte Carlo

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APA (6^th Edition):

Peterson, J. R. (2014). Exponentially-convergent Monte Carlo for the One-dimensional Transport Equation. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/152727

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Peterson, Jacob Ross. “Exponentially-convergent Monte Carlo for the One-dimensional Transport Equation.” 2014. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/152727.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Peterson, Jacob Ross. “Exponentially-convergent Monte Carlo for the One-dimensional Transport Equation.” 2014. Web. 16 Oct 2019.

Vancouver:

Peterson JR. Exponentially-convergent Monte Carlo for the One-dimensional Transport Equation. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/152727.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Peterson JR. Exponentially-convergent Monte Carlo for the One-dimensional Transport Equation. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/152727

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

3. Aldossary, Mubarak Nasser. The Value of Assessing Uncertainty.

Degree: 2016, Texas A&M University

URL: http://hdl.handle.net/1969.1/156856

► Despite the perception of lucrative earnings in the oil industry, various authors have noted that industry performance is routinely below expectations. For example, the average… (more)

▼ Despite the perception of lucrative earnings in the oil industry, various authors have noted that industry performance is routinely below expectations. For example, the average reported return for the industry was around 7% in the 1990s, even though a typical project hurdle rate was at least 15%. The underperformance is generally attributed to poor project evaluation and selection due to chronic bias. While a number of authors have investigated cognitive biases in oil and gas project evaluation, there have been few quantitative studies of the impact of biases on economic performance. Incomplete investigation and possible underestimation of the impact of biases in project evaluation and selection are at least partially responsible for persistence of these biases. The objectives of this work were to determine quantitatively the value of assessing uncertainty or, alternatively, the cost of underestimating uncertainty. This work presents a new framework for assessing the monetary impact of overconfidence bias and directional bias (i.e., optimism or pessimism) on portfolio performance. For moderate amounts of overconfidence and optimism, expected disappointment (having realized NPV less than estimated NPV) was 30-35% of estimated NPV for typical industry portfolios and optimization cases. Greater degrees of overconfidence and optimism resulted in expected disappointments approaching 100% of estimated NPV. Comparison of simulation results with expected industry performance in the 1990s, indicates that these greater degrees of overconfidence and optimism have been experienced in the industry. The value of reliably quantifying uncertainty is in reducing or eliminating expected disappointment and expected decision error (selecting the wrong projects), which is achieved by focusing primarily on elimination of overconfidence; other biases are taken care of in the process. Elimination of expected disappointment will improve industry performance overall to the extent that superior projects are available and better quantification of uncertainty allows identification of these superior projects. Advisors/Committee Members: McVay, Duane (advisor), Lee, John (committee member), Gildin, Eduardo (committee member), Mallick, Bani (committee member).

Subjects/Keywords: uncertainty; underestimating uncertainty; cognitive biases; overconfidence; optimism; pessimism; portfolios optimization; expected disappointment; optimizer's curse; expected decision error; directional bias

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APA (6^th Edition):

Aldossary, M. N. (2016). The Value of Assessing Uncertainty. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/156856

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Aldossary, Mubarak Nasser. “The Value of Assessing Uncertainty.” 2016. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/156856.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Aldossary, Mubarak Nasser. “The Value of Assessing Uncertainty.” 2016. Web. 16 Oct 2019.

Vancouver:

Aldossary MN. The Value of Assessing Uncertainty. [Internet] [Thesis]. Texas A&M University; 2016. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/156856.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Aldossary MN. The Value of Assessing Uncertainty. [Thesis]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/156856

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

4. Dorn, Mary Frances. Semiparametric Classification under a Forest Density Assumption.

Degree: 2017, Texas A&M University

URL: http://hdl.handle.net/1969.1/161361

► This dissertation proposes a new semiparametric approach for binary classification that exploits the modeling flexibility of sparse graphical models. This approach is based on non-parametrically… (more)

Subjects/Keywords: classification; nonparametric density estimation; forests; machine learning

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APA (6^th Edition):

Dorn, M. F. (2017). Semiparametric Classification under a Forest Density Assumption. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/161361

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Dorn, Mary Frances. “Semiparametric Classification under a Forest Density Assumption.” 2017. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/161361.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Dorn, Mary Frances. “Semiparametric Classification under a Forest Density Assumption.” 2017. Web. 16 Oct 2019.

Vancouver:

Dorn MF. Semiparametric Classification under a Forest Density Assumption. [Internet] [Thesis]. Texas A&M University; 2017. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/161361.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dorn MF. Semiparametric Classification under a Forest Density Assumption. [Thesis]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/161361

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

5. Li, Bo. An analysis of Texas rainfall data and asymptotic properties of space-time covariance estimators.

Degree: 2009, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-1868

► This dissertation includes two parts. Part 1 develops a geostatistical method to calibrate Texas NexRad rainfall estimates using rain gauge measurements. Part 2 explores the… (more)

▼ This dissertation includes two parts. Part 1 develops a geostatistical method to calibrate Texas NexRad rainfall estimates using rain gauge measurements. Part 2 explores the asymptotic joint distribution of sample space-time covariance estimators. The following two paragraphs briefly summarize these two parts, respectively. Rainfall is one of the most important hydrologic model inputs and is considered a random process in time and space. Rain gauges generally provide good quality data; however, they are usually too sparse to capture the spatial variability. Radar estimates provide a better spatial representation of rainfall patterns, but they are subject to substantial biases. Our calibration of radar estimates, using gauge data, takes season, rainfall type and rainfall amount into account, and is accomplished via a combination of threshold estimation, bias reduction, regression techniques and geostatistical procedures. We explore a varying-coefficient model to adapt to the temporal variability of rainfall. The methods are illustrated using Texas rainfall data in 2003, which includes WAR-88D radar-reflectivity data and the corresponding rain gauge measurements. Simulation experiments are carried out to evaluate the accuracy of our methodology. The superiority of the proposed method lies in estimating total rainfall as well as point rainfall amount. We study the asymptotic joint distribution of sample space-time covariance esti-mators of stationary random fields. We do this without any marginal or joint distri-butional assumptions other than mild moment and mixing conditions. We consider several situations depending on whether the observations are regularly or irregularly spaced, and whether one part or the whole domain of interest is fixed or increasing. A simulation experiment illustrates the asymptotic joint normality and the asymp- totic covariance matrix of sample space-time covariance estimators as derived. An extension of this part develops a nonparametric test for full symmetry, separability, Taylor's hypothesis and isotropy of space-time covariances. Advisors/Committee Members: Carroll, Raymond J. (advisor), Sherman, Michael (advisor), Eriksson, Marian (committee member), Mallick, Bani (committee member).

Subjects/Keywords: NexRad data; threshold; linear regression; variogram estimation; asymptotic normality; covariance; increasing domain asymptotics; random field

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APA (6^th Edition):

Li, B. (2009). An analysis of Texas rainfall data and asymptotic properties of space-time covariance estimators. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-1868

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Bo. “An analysis of Texas rainfall data and asymptotic properties of space-time covariance estimators.” 2009. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-1868.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Bo. “An analysis of Texas rainfall data and asymptotic properties of space-time covariance estimators.” 2009. Web. 16 Oct 2019.

Vancouver:

Li B. An analysis of Texas rainfall data and asymptotic properties of space-time covariance estimators. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1868.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li B. An analysis of Texas rainfall data and asymptotic properties of space-time covariance estimators. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1868

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

6. Tao, Qing. A Comparison of Waterflood Management Using Arrival Time Optimization and NPV Optimization.

Degree: 2011, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7238

► Waterflooding is currently the most commonly used method to improve oil recovery after primary depletion. The reservoir heterogeneity such as permeability distribution could negatively affect… (more)

▼ Waterflooding is currently the most commonly used method to improve oil recovery after primary depletion. The reservoir heterogeneity such as permeability distribution could negatively affect the performance of waterflooding. The presence of high permeability streaks could lead to an early water breakthrough at the producers and thus reduce the sweep efficiency in the field. One approach to counteract the impact of heterogeneity and to improve waterflood sweep efficiency is through optimal rate allocation to the injectors and producers. Through optimal rate control, we can manage the propagation of the flood front, delay water breakthrough at the producers and also increase the sweep and hence, the recovery efficiency. The arrival time optimization method uses a streamline-based method to calculate water arrival time sensitivities with respect to production and injection rates. It can also optimize sweep efficiency on multiple realizations to account for geological uncertainty. To extend the scope of this optimization method for more general conditions, this work utilized a finite difference simulator and streamline tracing software to conduct the optimization. Apart from sweep efficiency, another most widely used optimization method is to maximize the net present value (NPV) within a given time period. Previous efforts on optimization of waterflooding used optimal control theorem to allocate injection/production rates for fixed well configurations. The streamline-based approach gives the optimization result in a much more computationally efficient manner. In the present study, we compare the arrival time optimization and NPV optimization results to show their strengths and limitations. The NPV optimization uses a perturbation method to calculate the gradients. The comparison is conducted on a 4- spot synthetic case. Then we introduce the accelerated arrival time optimization which has an acceleration term in the objective function to speed up the oil production in the field. The proposed new approach has the advantage of considering both the sweep efficiency and net present value in the field. Advisors/Committee Members: Datta-Gupta, Akhil (advisor), Jafarpour, Behnam (committee member), Mallick, Bani (committee member).

Subjects/Keywords: waterflood management; arrival time optimization; NPV optimization; rate control; sweep efficiency

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APA (6^th Edition):

Tao, Q. (2011). A Comparison of Waterflood Management Using Arrival Time Optimization and NPV Optimization. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7238

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tao, Qing. “A Comparison of Waterflood Management Using Arrival Time Optimization and NPV Optimization.” 2011. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7238.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tao, Qing. “A Comparison of Waterflood Management Using Arrival Time Optimization and NPV Optimization.” 2011. Web. 16 Oct 2019.

Vancouver:

Tao Q. A Comparison of Waterflood Management Using Arrival Time Optimization and NPV Optimization. [Internet] [Thesis]. Texas A&M University; 2011. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7238.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tao Q. A Comparison of Waterflood Management Using Arrival Time Optimization and NPV Optimization. [Thesis]. Texas A&M University; 2011. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7238

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

7. Konomi, Bledar. Bayesian Spatial Modeling of Complex and High Dimensional Data.

Degree: 2012, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10267

► The main objective of this dissertation is to apply Bayesian modeling to different complex and high-dimensional spatial data sets. I develop Bayesian hierarchical spatial models… (more)

Subjects/Keywords: Object classification; Image segmentation; Nanoparticles; Markov-chain Monte-carlo; Bayesian shape analysis; Predictive process; Full-scale approximation; Bayesian treed Gaussian process

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Konomi, B. (2012). Bayesian Spatial Modeling of Complex and High Dimensional Data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10267

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Konomi, Bledar. “Bayesian Spatial Modeling of Complex and High Dimensional Data.” 2012. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10267.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Konomi, Bledar. “Bayesian Spatial Modeling of Complex and High Dimensional Data.” 2012. Web. 16 Oct 2019.

Vancouver:

Konomi B. Bayesian Spatial Modeling of Complex and High Dimensional Data. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10267.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Konomi B. Bayesian Spatial Modeling of Complex and High Dimensional Data. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10267

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

8. Xun, Xiaolei. Statistical Inference in Inverse Problems.

Degree: 2012, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10874

► Inverse problems have gained popularity in statistical research recently. This dissertation consists of two statistical inverse problems: a Bayesian approach to detection of small low… (more)

▼ Inverse problems have gained popularity in statistical research recently. This dissertation consists of two statistical inverse problems: a Bayesian approach to detection of small low emission sources on a large random background, and parameter estimation methods for partial differential equation (PDE) models. Source detection problem arises, for instance, in some homeland security applications. We address the problem of detecting presence and location of a small low emission source inside an object, when the background noise dominates. The goal is to reach the signal-to-noise ratio levels on the order of 10^-3. We develop a Bayesian approach to this problem in two-dimension. The method allows inference not only about the existence of the source, but also about its location. We derive Bayes factors for model selection and estimation of location based on Markov chain Monte Carlo simulation. A simulation study shows that with sufficiently high total emission level, our method can effectively locate the source. Differential equation (DE) models are widely used to model dynamic processes in many fields. The forward problem of solving equations for given parameters that define the DEs has been extensively studied in the past. However, the inverse problem of estimating parameters based on observed state variables is relatively sparse in the statistical literature, and this is especially the case for PDE models. We propose two joint modeling schemes to solve for constant parameters in PDEs: a parameter cascading method and a Bayesian treatment. In both methods, the unknown functions are expressed via basis function expansion. For the parameter cascading method, we develop the algorithm to estimate the parameters and derive a sandwich estimator of the covariance matrix. For the Bayesian method, we develop the joint model for data and the PDE, and describe how the Markov chain Monte Carlo technique is employed to make posterior inference. A straightforward two-stage method is to first fit the data and then to estimate parameters by the least square principle. The three approaches are illustrated using simulated examples and compared via simulation studies. Simulation results show that the proposed methods outperform the two-stage method. Advisors/Committee Members: Carroll, Raymond J. (advisor), Mallick, Bani K. (advisor), Sang, Huiyan (committee member), Kuchment, Peter (committee member).

Subjects/Keywords: Inverse problems; Bayesian method; Source detection; Parameter estimation; Parameter cascading; Partial differential equations.

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APA (6^th Edition):

Xun, X. (2012). Statistical Inference in Inverse Problems. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10874

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Xun, Xiaolei. “Statistical Inference in Inverse Problems.” 2012. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10874.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Xun, Xiaolei. “Statistical Inference in Inverse Problems.” 2012. Web. 16 Oct 2019.

Vancouver:

Xun X. Statistical Inference in Inverse Problems. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10874.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xun X. Statistical Inference in Inverse Problems. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10874

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

9. Zhang, Lin. Application of Bayesian Hierarchical Models in Genetic Data Analysis.

Degree: 2012, Texas A&M University

URL: http://hdl.handle.net/1969.1/148056

► Genetic data analysis has been capturing a lot of attentions for understanding the mechanism of the development and progressing of diseases like cancers, and is… (more)

▼ Genetic data analysis has been capturing a lot of attentions for understanding the mechanism of the development and progressing of diseases like cancers, and is crucial in discovering genetic markers and treatment targets in medical research. This dissertation focuses on several important issues in genetic data analysis, graphical network modeling, feature selection, and covariance estimation. First, we develop a gene network modeling method for discrete gene expression data, produced by technologies such as serial analysis of gene expression and RNA sequencing experiment, which generate counts of mRNA transcripts in cell samples. We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution. We derive the gene network structures by selecting covariance matrices of the Gaussian distribution with a hyper-inverse Wishart prior. We incorporate prior network models based on Gene Ontology information, which avails existing biological information on the genes of interest. Next, we consider a variable selection problem, where the variables have natural grouping structures, with application to analysis of chromosomal copy number data. The chromosomal copy number data are produced by molecular inversion probes experiments which measure probe-specific copy number changes. We propose a novel Bayesian variable selection method, the hierarchical structured variable se- lection (HSVS) method, which accounts for the natural gene and probe-within-gene architecture to identify important genes and probes associated with clinically relevant outcomes. We propose the HSVS model for grouped variable selection, where simultaneous selection of both groups and within-group variables is of interest. The HSVS model utilizes a discrete mixture prior distribution for group selection and group-specific Bayesian lasso hierarchies for variable selection within groups. We further provide methods for accounting for serial correlations within groups that incorporate Bayesian fused lasso methods for within-group selection. Finally, we propose a Bayesian method of estimating high-dimensional covariance matrices that can be decomposed into a low rank and sparse component. This covariance structure has a wide range of applications including factor analytical model and random effects model. We model the covariance matrices with the decomposition structure by representing the covariance model in the form of a factor analytic model where the number of latent factors is unknown. We introduce binary indicators for estimating the rank of the low rank component combined with a Bayesian graphical lasso method for estimating the sparse component. We further extend our method to a graphical factor analytic model where the graphical model of the residuals is of interest. We achieve sparse estimation of the inverse covariance of the residuals in the graphical factor model by employing a hyper-inverse Wishart prior method for a decomposable graph and a Bayesian… Advisors/Committee Members: Mallick, Bani K (advisor), Baladandayuthapani, Veera (advisor), Carroll, Raymond J (committee member), Adams, Garry (committee member).

Subjects/Keywords: covariance estimation; feature selection; graphical network modeling; genetic data analysis; Bayesian hierarchical model

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhang, L. (2012). Application of Bayesian Hierarchical Models in Genetic Data Analysis. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/148056

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zhang, Lin. “Application of Bayesian Hierarchical Models in Genetic Data Analysis.” 2012. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/148056.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zhang, Lin. “Application of Bayesian Hierarchical Models in Genetic Data Analysis.” 2012. Web. 16 Oct 2019.

Vancouver:

Zhang L. Application of Bayesian Hierarchical Models in Genetic Data Analysis. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/148056.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang L. Application of Bayesian Hierarchical Models in Genetic Data Analysis. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/148056

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

10. Olalotiti-Lawal, Feyisayo. Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification.

Degree: 2013, Texas A&M University

URL: http://hdl.handle.net/1969.1/150964

► Rapid economic evaluations of investment alternatives in the oil and gas industry are typically contingent on fast and credible evaluations of reservoir models to make… (more)

▼ Rapid economic evaluations of investment alternatives in the oil and gas industry are typically contingent on fast and credible evaluations of reservoir models to make future forecasts. It is often important to also quantify inherent risks and uncertainties in these evaluations. These ideally require several full-scale numerical simulations which is time consuming, impractical, if not impossible to do with conventional (Finite Difference) simulators in real life situations. In this research, the aim will be to improve on the efficiencies associated with these tasks. This involved exploring the applications of Fast Marching Methods (FMM) in both conventional and unconventional reservoir characterization problems. In this work, we first applied the FMM for rapidly ranking multiple equi-probable geologic models. We demonstrated the suitability of drainage volume, efficiently calculated using FMM, as a surrogate parameter for field-wide cumulative oil production (FOPT). The probability distribution function (PDF) of the surrogate parameter was point-discretized to obtain 3 representative models for full simulations. Using the results from the simulations, the PDF of the reservoir performance parameter was constructed. Also, we investigated the applicability of a higher-order-moment-preserving approach which resulted in better uncertainty quantification over the traditional model selection methods. Next we applied the FMM for a hydraulically fractured tight oil reservoir model calibration problem. We specifically applied the FMM geometric pressure approximation as a proxy for rapidly evaluating model proposals in a two-stage Markov Chain Monte Carlo (MCMC) algorithm. Here, we demonstrated the FMM-based proxy as a suitable proxy for evaluating model proposals. We obtained results showing a significant improvement in the efficiency compared to conventional single stage MCMC algorithm. Also in this work, we investigated the possibility of enhancing the computational efficiency for calculating the pressure field for both conventional and unconventional reservoirs using FMM. Good approximations of the steady state pressure distributions were obtained for homogeneous conventional waterflood systems. In unconventional system, we also recorded slight improvement in computational efficiency using FMM pressure approximations as initial guess in pressure solvers. Advisors/Committee Members: Datta-Gupta, Akhil (advisor), King, Michael (committee member), Mallick, Bani (committee member).

Subjects/Keywords: Fast Marching Method; Geologic Model Ranking; Model Calibration; Two-Stage MCMC; Geometric Pressure Approximation

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APA (6^th Edition):

Olalotiti-Lawal, F. (2013). Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/150964

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Olalotiti-Lawal, Feyisayo. “Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification.” 2013. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/150964.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Olalotiti-Lawal, Feyisayo. “Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification.” 2013. Web. 16 Oct 2019.

Vancouver:

Olalotiti-Lawal F. Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/150964.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Olalotiti-Lawal F. Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/150964

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

11. De, Debkumar. Essays on Bayesian Time Series and Variable Selection.

Degree: 2014, Texas A&M University

URL: http://hdl.handle.net/1969.1/152793

► Estimating model parameters in dynamic model continues to be challenge. In my dissertation, we have introduced a Stochastic Approximation based parameter estimation approach under Ensemble… (more)

Subjects/Keywords: Ensemble Kalman Filter; Stochastic Approximation; Non-parametric Regression; Matrix variate regression; Variable selection

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APA (6^th Edition):

De, D. (2014). Essays on Bayesian Time Series and Variable Selection. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/152793

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

De, Debkumar. “Essays on Bayesian Time Series and Variable Selection.” 2014. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/152793.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

De, Debkumar. “Essays on Bayesian Time Series and Variable Selection.” 2014. Web. 16 Oct 2019.

Vancouver:

De D. Essays on Bayesian Time Series and Variable Selection. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/152793.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

De D. Essays on Bayesian Time Series and Variable Selection. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/152793

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

12. Roh, Soojin. Robust Ensemble Kalman Filters and Localization for Multiple State Variables.

Degree: 2014, Texas A&M University

URL: http://hdl.handle.net/1969.1/153268

► Ensemble Kalman filters (EnKF) is a statistical technique used to estimate the state of a nonlinear spatio-temporal dynamical system. This dissertation consists of three parts.… (more)

Subjects/Keywords: Ensemble Kalman filter; Robust; Multivariate Localization

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APA (6^th Edition):

Roh, S. (2014). Robust Ensemble Kalman Filters and Localization for Multiple State Variables. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153268

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Roh, Soojin. “Robust Ensemble Kalman Filters and Localization for Multiple State Variables.” 2014. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/153268.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Roh, Soojin. “Robust Ensemble Kalman Filters and Localization for Multiple State Variables.” 2014. Web. 16 Oct 2019.

Vancouver:

Roh S. Robust Ensemble Kalman Filters and Localization for Multiple State Variables. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/153268.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Roh S. Robust Ensemble Kalman Filters and Localization for Multiple State Variables. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153268

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

13. Wang, Yanqing. Relative Risks Analysis in Nutritional Epidemiology.

Degree: 2014, Texas A&M University

URL: http://hdl.handle.net/1969.1/153663

► Motivated by a logistic regression problem involving diet and cancer, we reconsider the problem of forming a confidence interval for the ratio of two location… (more)

▼ Motivated by a logistic regression problem involving diet and cancer, we reconsider the problem of forming a confidence interval for the ratio of two location parameters. We develop a new methodology, which we call the Direct Integral Method for Ratios (DIMER). In simulations, we compare this method to many others, including Wald's method, Fieller's interval, Hayya's method, the nonparametric bootstrap and the parametric bootstrap. These simulations show that, generally, DIMER more closely achieves the nominal confidence level, and in those cases that the other methods achieve the nominal levels, DIMER generally has smaller confidence interval lengths. We also show that DIMER eliminates the probability of infinite length or enormous length confidence intervals, something that can occur in Fieller's interval. Furthermore, we study the real Healthy Eating Index-2005 (HEI-2005) data set from the NIH-AARP Study of Diet and Health, consider a weighted logistic regression model in which there are multiple subpopulations, and multiple diseases within each subpopulation. Based on this model, we present six different approaches to form the confidence intervals for the relative risks of different diseases in different subpopulations, including DIMER. The asymptotic distributions of the estimates for the log(relative risks) by the maximum likelihood and the nonparametric bootstrap method are provided. Next, the algorithms are presented to perform hypothesis tests and likelihood ratio tests to check there are significant differences between our proposed model and the other three logistic regression models or not. In addition, the adaptive lasso and an estimator with bounded constrains are described for variable selection and a novel algorithm to solve the nonlinear regression model with L1 norm penalty is proposed. The application of all those methods to the HEI-2005 data are illustrated. Additionally, we expand the linear function of nutrition components inside the logistic regression model to a nonlinear case. More than that, we consider there are some limitations from the knowledge of biology and nutrition and propose a logistic regression model involving I-spline basis functions and an algorithm to solve it. Application to the real HEI-200d data set and comparison to a logistic model with total HEI scores are also presented. Advisors/Committee Members: Carroll, Raymond (advisor), Mallick, Bani (advisor), Baladandayuthapani, Veera (committee member), Braga-Neto, Ulisses (committee member).

Subjects/Keywords: Adaptive Lasso; DIMER; Direct Integral Method for Ratios; HEI-2005; I-spline; Relative Risk; Variable Selection.

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APA (6^th Edition):

Wang, Y. (2014). Relative Risks Analysis in Nutritional Epidemiology. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153663

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wang, Yanqing. “Relative Risks Analysis in Nutritional Epidemiology.” 2014. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/153663.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wang, Yanqing. “Relative Risks Analysis in Nutritional Epidemiology.” 2014. Web. 16 Oct 2019.

Vancouver:

Wang Y. Relative Risks Analysis in Nutritional Epidemiology. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/153663.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Y. Relative Risks Analysis in Nutritional Epidemiology. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153663

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

14. Xia, Xiaoyang. History-Matching Production Data Using Ensemble Smoother with Multiple Data Assimilation: A Comparative Study.

Degree: 2014, Texas A&M University

URL: http://hdl.handle.net/1969.1/154226

► Reservoir simulation models are generated by petroleum engineers to optimize field operation and production, thus maximizing oil recovery. History matching methods are extensively used for… (more)

Subjects/Keywords: Ensemble Smoother with Multiple Data Assimilation; History Matching

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APA (6^th Edition):

Xia, X. (2014). History-Matching Production Data Using Ensemble Smoother with Multiple Data Assimilation: A Comparative Study. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/154226

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Xia, Xiaoyang. “History-Matching Production Data Using Ensemble Smoother with Multiple Data Assimilation: A Comparative Study.” 2014. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/154226.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Xia, Xiaoyang. “History-Matching Production Data Using Ensemble Smoother with Multiple Data Assimilation: A Comparative Study.” 2014. Web. 16 Oct 2019.

Vancouver:

Xia X. History-Matching Production Data Using Ensemble Smoother with Multiple Data Assimilation: A Comparative Study. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/154226.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xia X. History-Matching Production Data Using Ensemble Smoother with Multiple Data Assimilation: A Comparative Study. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/154226

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

15. Rahman, Shahina. Efficient Nonparametric and Semiparametric Regression Methods with application in Case-Control Studies.

Degree: 2015, Texas A&M University

URL: http://hdl.handle.net/1969.1/155719

► Regression Analysis is one of the most important tools of statistics which is widely used in other scientific fields for projection and modeling of association… (more)

▼ Regression Analysis is one of the most important tools of statistics which is widely used in other scientific fields for projection and modeling of association between two variables. Nowadays with modern computing techniques and super high performance devices, regression analysis on multiple dimensions has become an important issue. Our task is to address the issue of modeling with no assumption on the mean and the variance structure and further with no assumption on the error distribution. In other words, we focus on developing robust semiparametric and nonparamteric regression problems. In modern genetic epidemiological association studies, it is often important to investigate the relationships among the potential covariates related to disease in case-control data, a study known as "Secondary Analysis". First we focus to model the association between the potential covariates in univariate dimension nonparametrically. Then we focus to model the association in mulivariate set up by assuming a convenient and popular multivariate semiparametric model, known as Single-Index Model. The secondary analysis of case-control studies is particularly challenging due to multiple reasons (a) the case-control sample is not a random sample, (b) the logistic intercept is practically not identifiable and (c) misspecification of error distribution leads to inconsistent results. For rare disease, controls (individual free of disease) are typically used for valid estimation. However, numerous publication are done to utilize the entire case-control sample (including the diseased individual) to increase the efficiency. Previous work in this context has either specified a fully parametric distribution for regression errors or specified a homoscedastic distribution for the regression errors or have assumed parametric forms on the regression mean. In the first chapter we focus on to predict an univariate covariate Y by another potential univariate covariate X neither by any parametric form on the mean function nor by any distributional assumption on error, hence addressing potential heteroscedasticity, a problem which has not been studied before. We develop a tilted Kernel based estimator which is a first attempt to model the mean function nonparametrically in secondary analysis. In the following chapters, we focus on i.i.d samples to model both the mean and variance function for predicting Y by multiple covariates X without assuming any form on the regression mean. In particular we model Y by a single-index model m(X^T ?), where ? is a single-index vector and m is unspecified. We also model the variance function by another flexible single index model. We develop a practical and readily applicable Bayesian methodology based on penalized spline and Markov Chain Monte Carlo (MCMC) both in i.i.d set up and in case-control set up. For efficient estimation, we model the error distribution by a Dirichlet process mixture models of Normals (DPMM). In numerical examples, we illustrate the finite sample performance of the posterior estimates for… Advisors/Committee Members: Carroll, Raymond J. (advisor), Ma, Yanyuan (advisor), Smith, Roger (committee member), Harknett, Urshi Mueller (committee member), Mallick, Bani K. (committee member).

Subjects/Keywords: Bayesian Methods; Case-control; Dirichlet Process of Mixture Model; Efficiency; Heteroscedasticity; Kernel estimation; Nonparametric; P-splines; Robust; Secondary Analysis; Semiparametric; Single-Index Model

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APA (6^th Edition):

Rahman, S. (2015). Efficient Nonparametric and Semiparametric Regression Methods with application in Case-Control Studies. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/155719

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Rahman, Shahina. “Efficient Nonparametric and Semiparametric Regression Methods with application in Case-Control Studies.” 2015. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/155719.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Rahman, Shahina. “Efficient Nonparametric and Semiparametric Regression Methods with application in Case-Control Studies.” 2015. Web. 16 Oct 2019.

Vancouver:

Rahman S. Efficient Nonparametric and Semiparametric Regression Methods with application in Case-Control Studies. [Internet] [Thesis]. Texas A&M University; 2015. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/155719.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rahman S. Efficient Nonparametric and Semiparametric Regression Methods with application in Case-Control Studies. [Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/155719

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

16. Wei, Rubin. Highly Nonlinear Measurement Error Models in Nutritional Epidemiology.

Degree: 2014, Texas A&M University

URL: http://hdl.handle.net/1969.1/161236

► This dissertation consists of two main projects in the area of measurement error models with application in nutritional epidemiology. The first project studies the application… (more)

▼ This dissertation consists of two main projects in the area of measurement error models with application in nutritional epidemiology. The first project studies the application of moment reconstruction and moment- adjusted imputation in the context of nonlinear Berkson-type measurement error. The idea of moment reconstruction and moment adjusted imputation, like regression calibration, is to replace the unobserved variable of interest which is subject to measurement error with a proxy, which can be used in a variety of subsequent analyses, without redoing the measurement error model each time a different downstream analysis is performed. However, both methods essentially require the homoscedastic classical measurement error model or non-classical model that can be easily reduced to a classical one. In the first project, we deal with a case where the measurement error structure is of nonlinear Berkson-type, and develop analogues of moment reconstruction and moment-adjusted imputation for this case. We use National Institutes of Health-AARP Diet and Health Study, where the latent variable is a dietary pattern score called the Healthy Eating Index-2005, and simulations to illustrate the methods. The numerical results show the promise of these methods in the nonlinear Berkson-type measurement error context. In the second project, we consider measurement error models for two variables observed repeatedly and subject to measurement error. One variable is continuous but positive, while the other variable is a mixture of continuous and zero measurements. This second variable has two sources of zeros. The first source is episodic zeros, wherein some of the measurements for an individual may be zero and others positive. The second source is hard zeros, i.e., some individuals will always report zero. An example is the consumption of alcohol from alcoholic beverages: some individuals consume alcoholic beverages episodically, while others never consume alcoholic beverages. However, with a small number of repeated measurements from individuals, it is not possible to determine those that are episodic zeros and those that are hard zeros. We develop a new measurement error model for this problem, and use Bayesian methods to t it. We also contrast our approach for a single variable which is subject to excess zeros, with those methods that have been developed for a single variable and proven to be somewhat numerically unstable. Simulations and data analyses of two studies are used to show that the new method gives more realistic and numerically stable results than the maximum likelihood approach. Advisors/Committee Members: Carroll, Raymond J. (advisor), Longnecker, Michael T. (committee member), Turner, Nancy D. (committee member), Mallick, Bani K. (committee member).

Subjects/Keywords: Measurement error; Berkson-type error; Latent variable models; Moment reconstruction; Bayesian methods; Hard zeroes; Zero-inflation; Mixed models; Nutritional epidemiology; Usual intake; Never-consumers

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APA (6^th Edition):

Wei, R. (2014). Highly Nonlinear Measurement Error Models in Nutritional Epidemiology. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/161236

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wei, Rubin. “Highly Nonlinear Measurement Error Models in Nutritional Epidemiology.” 2014. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/161236.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wei, Rubin. “Highly Nonlinear Measurement Error Models in Nutritional Epidemiology.” 2014. Web. 16 Oct 2019.

Vancouver:

Wei R. Highly Nonlinear Measurement Error Models in Nutritional Epidemiology. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/161236.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wei R. Highly Nonlinear Measurement Error Models in Nutritional Epidemiology. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/161236

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

17. Cho, Jung Jin. On the robustness of clustered sensor networks.

Degree: 2009, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2527

► Smart devices with multiple on-board sensors, networked through wired or wireless links, are distributed in physical systems and environments. Broad applications of such sensor networks… (more)

Subjects/Keywords: Robustness; Sensor Network; Matroid; Cogirth; Breakdown Point; Robust Estimation

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APA (6^th Edition):

Cho, J. J. (2009). On the robustness of clustered sensor networks. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2527

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Cho, Jung Jin. “On the robustness of clustered sensor networks.” 2009. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2527.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Cho, Jung Jin. “On the robustness of clustered sensor networks.” 2009. Web. 16 Oct 2019.

Vancouver:

Cho JJ. On the robustness of clustered sensor networks. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2527.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cho JJ. On the robustness of clustered sensor networks. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2527

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

18. Dostert, Paul Francis. Uncertainty quantification using multiscale methods for porous media flows.

Degree: 2009, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2532

► In this dissertation we discuss numerical methods used for uncertainty quantifi- cation applications to flow in porous media. We consider stochastic flow equations that contain… (more)

Subjects/Keywords: Upscaling; MCMC

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APA (6^th Edition):

Dostert, P. F. (2009). Uncertainty quantification using multiscale methods for porous media flows. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2532

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Dostert, Paul Francis. “Uncertainty quantification using multiscale methods for porous media flows.” 2009. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2532.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Dostert, Paul Francis. “Uncertainty quantification using multiscale methods for porous media flows.” 2009. Web. 16 Oct 2019.

Vancouver:

Dostert PF. Uncertainty quantification using multiscale methods for porous media flows. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2532.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dostert PF. Uncertainty quantification using multiscale methods for porous media flows. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2532

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

19. Lobach, Iryna. Case-control studies of genetic and environmental factors with error in measurement of environmental factors.

Degree: 2009, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-1026

► It is widely believed that risks of many complex diseases are determined by genetic susceptibilities, including environmental exposures, and their interaction. Chatterjee and Carroll (2005)… (more)

Subjects/Keywords: Case-control studies; gene-environment interactions

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APA (6^th Edition):

Lobach, I. (2009). Case-control studies of genetic and environmental factors with error in measurement of environmental factors. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-1026

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lobach, Iryna. “Case-control studies of genetic and environmental factors with error in measurement of environmental factors.” 2009. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-1026.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lobach, Iryna. “Case-control studies of genetic and environmental factors with error in measurement of environmental factors.” 2009. Web. 16 Oct 2019.

Vancouver:

Lobach I. Case-control studies of genetic and environmental factors with error in measurement of environmental factors. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1026.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lobach I. Case-control studies of genetic and environmental factors with error in measurement of environmental factors. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1026

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

20. Gold, David L. Bayesian learning in bioinformatics.

Degree: 2009, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-1624

► Life sciences research is advancing in breadth and scope, affecting many areas of life including medical care and government policy. The field of Bioinformatics, in… (more)

▼ Life sciences research is advancing in breadth and scope, affecting many areas of life including medical care and government policy. The field of Bioinformatics, in particular, is growing very rapidly with the help of computer science, statistics, applied mathematics, and engineering. New high-throughput technologies are making it possible to measure genomic variation across phenotypes in organisms at costs that were once inconceivable. In conjunction, and partly as a consequence, massive amounts of information about the genomes of many organisms are becoming accessible in the public domain. Some of the important and exciting questions in the post-genomics era are how to integrate all of the information available from diverse sources. Learning in complex systems biology requires that information be shared in a natural and interpretable way, to integrate knowledge and data. The statistical sciences can support the advancement of learning in Bioinformatics in many ways, not the least of which is by developing methodologies that can support the synchronization of efforts across sciences, offering real-time learning tools that can be shared across many fields from basic science to the clinical applications. This research is an introduction to several current research problems in Bioinformatics that addresses integration of information, and discusses statistical methodologies from the Bayesian school of thought that may be applied. Bayesian statistical methodologies are proposed to integrate biological knowledge and improve statistical inference for three relevant Bioinformatics applications: gene expression arrays, BAC and aCGH arrays, and real-time gene expression experiments. A unified Bayesian model is proposed to perform detection of genes and gene classes, defined from historical pathways, with gene expression arrays. A novel Bayesian statistical method is proposed to infer chromosomal copy number aberrations in clinical populations with BAC or aCGH experiments. A theoretical model is proposed, motivated from historical work in mathematical biology, for inference with real-time gene expression experiments, and fit with Bayesian methods. Simulation and case studies show that Bayesian methodologies show great promise to improve the way we learn with high-throughput Bioinformatics experiments. Advisors/Committee Members: Mallick, Bani (advisor), Coombes, Kevin R. (committee member), Dahl, David (committee member), Dougherty, Edward (committee member), Liang, Faming (committee member).

Subjects/Keywords: Bayesian; Bioinformatics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Gold, D. L. (2009). Bayesian learning in bioinformatics. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-1624

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Gold, David L. “Bayesian learning in bioinformatics.” 2009. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-1624.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Gold, David L. “Bayesian learning in bioinformatics.” 2009. Web. 16 Oct 2019.

Vancouver:

Gold DL. Bayesian learning in bioinformatics. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1624.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gold DL. Bayesian learning in bioinformatics. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-1624

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

21. Devegowda, Deepak. Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application.

Degree: 2010, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-2916

► The goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models… (more)

▼ The goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models and the assessment of the underlying uncertainties. A considerable body of research work and enhanced computational capabilities have led to an increased application of robust and efficient history matching algorithms to condition reservoir models to dynamic data. Moreover, there has been a shift towards generating multiple plausible reservoir models in recognition of the significance of the associated uncertainties. This provides for uncertainty analysis in reservoir performance forecasts, enabling better management decisions for reservoir development. Additionally, the increased deployment of permanent well sensors and downhole monitors has led to an increasing interest in maintaining 'live' models that are current and consistent with historical observations. One such data assimilation approach that has gained popularity in the recent past is the Ensemble Kalman Filter (EnKF) (Evensen 2003). It is a Monte Carlo approach to generate a suite of plausible subsurface models conditioned to previously obtained measurements. One advantage of the EnKF is its ability to integrate different types of data at different scales thereby allowing for a framework where all available dynamic data is simultaneously or sequentially utilized to improve estimates of the reservoir model parameters. Of particular interest is the use of partitioning tracer data to infer the location and distribution of target un-swept oil. Due to the difficulty in differentiating the relative effects of spatial variations in fractional flow and fluid saturations and partitioning coefficients on the tracer response, interpretation of partitioning tracer responses is particularly challenging in the presence of mobile oil saturations. The purpose of this research is to improve the performance of the EnKF in parameter estimation for reservoir characterization studies without the use of a large ensemble size so as to keep the algorithm efficient and computationally inexpensive for large, field-scale models. To achieve this, we propose the use of streamline-derived information to mitigate problems associated with the use of the EnKF with small sample sizes and non-linear dynamics in non-Gaussian settings. Following this, we present the application of the EnKF for interpretation of partitioning tracer tests specifically to obtain improved estimates of the spatial distribution of target oil. Advisors/Committee Members: Datta-Gupta, Akhil (advisor), Holditch, Steve (committee member), Ghassemi, Ahmed (committee member), Mallick, Bani K. (committee member).

Subjects/Keywords: EnKF; Data Assimilation; Covariance Localization; Reservoir Characterization; Streamlines

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APA (6^th Edition):

Devegowda, D. (2010). Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-2916

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Devegowda, Deepak. “Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application.” 2010. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-2916.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Devegowda, Deepak. “Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application.” 2010. Web. 16 Oct 2019.

Vancouver:

Devegowda D. Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application. [Internet] [Thesis]. Texas A&M University; 2010. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-2916.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Devegowda D. Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application. [Thesis]. Texas A&M University; 2010. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-2916

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

22. Ghosh, Souparno. Copula Based Hierarchical Bayesian Models.

Degree: 2010, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-7160

► The main objective of our study is to employ copula methodology to develop Bayesian hierarchical models to study the dependencies exhibited by temporal, spatial and… (more)

▼ The main objective of our study is to employ copula methodology to develop Bayesian hierarchical models to study the dependencies exhibited by temporal, spatial and spatio-temporal processes. We develop hierarchical models for both discrete and continuous outcomes. In doing so we expect to address the dearth of copula based Bayesian hierarchical models to study hydro-meteorological events and other physical processes yielding discrete responses. First, we present Bayesian methods of analysis for longitudinal binary outcomes using Generalized Linear Mixed models (GLMM). We allow flexible marginal association among the repeated outcomes from different time-points. An unique property of this copula-based GLMM is that if the marginal link function is integrated over the distribution of the random effects, its form remains same as that of the conditional link function. This unique property enables us to retain the physical interpretation of the fixed effects under conditional and marginal model and yield proper posterior distribution. We illustrate the performance of the posited model using real life AIDS data and demonstrate its superiority over the traditional Gaussian random effects model. We develop a semiparametric extension of our GLMM and re-analyze the data from the AIDS study. Next, we propose a general class of models to handle non-Gaussian spatial data. The proposed model can deal with geostatistical data that can accommodate skewness, tail-heaviness, multimodality. We fix the distribution of the marginal processes and induce dependence via copulas. We illustrate the superior predictive performance of our approach in modeling precipitation data as compared to other kriging variants. Thereafter, we employ mixture kernels as the copula function to accommodate non-stationary data. We demonstrate the adequacy of this non-stationary model by analyzing permeability data. In both cases we perform extensive simulation studies to investigate the performances of the posited models under misspecification. Finally, we take up the important problem of modeling multivariate extreme values with copulas. We describe, in detail, how dependences can be induced in the block maxima approach and peak over threshold approach by an extreme value copula. We prove the ability of the posited model to handle both strong and weak extremal dependence and derive the conditions for posterior propriety. We analyze the extreme precipitation events in the continental United States for the past 98 years and come up with a suite of predictive maps. Advisors/Committee Members: Mallick, Bani K. (advisor), Huang, Jianhua (committee member), Genton, Marc G. (committee member), Saravanan, Ramalingam (committee member).

Subjects/Keywords: Hierarchical model; Copula; Geostatistics; Extreme value processes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ghosh, S. (2010). Copula Based Hierarchical Bayesian Models. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-7160

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ghosh, Souparno. “Copula Based Hierarchical Bayesian Models.” 2010. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-7160.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ghosh, Souparno. “Copula Based Hierarchical Bayesian Models.” 2010. Web. 16 Oct 2019.

Vancouver:

Ghosh S. Copula Based Hierarchical Bayesian Models. [Internet] [Thesis]. Texas A&M University; 2010. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-7160.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ghosh S. Copula Based Hierarchical Bayesian Models. [Thesis]. Texas A&M University; 2010. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-7160

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

23. Talluri, Rajesh. Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.

Degree: 2012, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828

► We propose Bayesian methods for estimating the precision matrix in Gaussian graphical models. The methods lead to sparse and adaptively shrunk estimators of the precision… (more)

▼ We propose Bayesian methods for estimating the precision matrix in Gaussian graphical models. The methods lead to sparse and adaptively shrunk estimators of the precision matrix, and thus conduct model selection and estimation simultaneously. Our methods are based on selection and shrinkage priors leading to parsimonious parameterization of the precision (inverse covariance) matrix, which is essential in several applications in learning relationships among the variables. In Chapter I, we employ the Laplace prior on the off-diagonal element of the precision matrix, which is similar to the lasso model in a regression context. This type of prior encourages sparsity while providing shrinkage estimates. Secondly we introduce a novel type of selection prior that develops a sparse structure of the precision matrix by making most of the elements exactly zero, ensuring positive-definiteness. In Chapter II we extend the above methods to perform classification. Reverse-phase protein array (RPPA) analysis is a powerful, relatively new platform that allows for high-throughput, quantitative analysis of protein networks. One of the challenges that currently limits the potential of this technology is the lack of methods that allows for accurate data modeling and identification of related networks and samples. Such models may improve the accuracy of biological sample classification based on patterns of protein network activation, and provide insight into the distinct biological relationships underlying different cancers. We propose a Bayesian sparse graphical modeling approach motivated by RPPA data using selection priors on the conditional relationships in the presence of class information. We apply our methodology to an RPPA data set generated from panels of human breast cancer and ovarian cancer cell lines. We demonstrate that the model is able to distinguish the different cancer cell types more accurately than several existing models and to identify differential regulation of components of a critical signaling network (the PI3K-AKT pathway) between these cancers. This approach represents a powerful new tool that can be used to improve our understanding of protein networks in cancer. In Chapter III we extend these methods to mixtures of Gaussian graphical models for clustered data, with each mixture component being assumed Gaussian with an adaptive covariance structure. We model the data using Dirichlet processes and finite mixture models and discuss appropriate posterior simulation schemes to implement posterior inference in the proposed models, including the evaluation of normalizing constants that are functions of parameters of interest which are a result of the restrictions on the correlation matrix. We evaluate the operating characteristics of our method via simulations, as well as discuss examples based on several real data sets. Advisors/Committee Members: Mallick, Bani K. (advisor), Baladandayuthapani, Veerabhadran (committee member), Hart, Jeffrey D. (committee member), Datta, Aniruddha (committee member).

Subjects/Keywords: Bayesian; Gaussian Graphical Models; Covariance Selection; Mixture Models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Talluri, R. (2012). Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Talluri, Rajesh. “Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.” 2012. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Talluri, Rajesh. “Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.” 2012. Web. 16 Oct 2019.

Vancouver:

Talluri R. Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Talluri R. Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

24. Xia, Haifeng. Bayesian Hierarchical Model for Combining Two-resolution Metrology Data.

Degree: 2010, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-164

► This dissertation presents a Bayesian hierarchical model to combine two-resolution metrology data for inspecting the geometric quality of manufactured parts. The high- resolution data points… (more)

▼ This dissertation presents a Bayesian hierarchical model to combine two-resolution metrology data for inspecting the geometric quality of manufactured parts. The high- resolution data points are scarce, and thus scatter over the surface being measured, while the low-resolution data are pervasive, but less accurate or less precise. Combining the two datasets could supposedly make a better prediction of the geometric surface of a manufactured part than using a single dataset. One challenge in combining the metrology datasets is the misalignment which exists between the low- and high-resolution data points. This dissertation attempts to provide a Bayesian hierarchical model that can handle such misaligned datasets, and includes the following components: (a) a Gaussian process for modeling metrology data at the low-resolution level; (b) a heuristic matching and alignment method that produces a pool of candidate matches and transformations between the two datasets; (c) a linkage model, conditioned on a given match and its associated transformation, that connects a high-resolution data point to a set of low-resolution data points in its neighborhood and makes a combined prediction; and finally (d) Bayesian model averaging of the predictive models in (c) over the pool of candidate matches found in (b). This Bayesian model averaging procedure assigns weights to different matches according to how much they support the observed data, and then produces the final combined prediction of the surface based on the data of both resolutions. The proposed method improves upon the methods of using a single dataset as well as a combined prediction without addressing the misalignment problem. This dissertation demonstrates the improvements over alternative methods using both simulated data and the datasets from a milled sine-wave part, measured by two coordinate measuring machines of different resolutions, respectively. Advisors/Committee Members: Ding, Yu (advisor), Mallick, Bani K. (committee member), Peters, Brett A. (committee member), Wang, Jyhwen (committee member).

Subjects/Keywords: Bayesian hierarchical model; Bayesian model averaging; Data combining; Gaussian process; Misalignment; Two-resolution data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Xia, H. (2010). Bayesian Hierarchical Model for Combining Two-resolution Metrology Data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-164

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Xia, Haifeng. “Bayesian Hierarchical Model for Combining Two-resolution Metrology Data.” 2010. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-164.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Xia, Haifeng. “Bayesian Hierarchical Model for Combining Two-resolution Metrology Data.” 2010. Web. 16 Oct 2019.

Vancouver:

Xia H. Bayesian Hierarchical Model for Combining Two-resolution Metrology Data. [Internet] [Thesis]. Texas A&M University; 2010. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-164.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xia H. Bayesian Hierarchical Model for Combining Two-resolution Metrology Data. [Thesis]. Texas A&M University; 2010. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-164

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

25. Hetzler, Adam C. Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem.

Degree: 2013, Texas A&M University

URL: http://hdl.handle.net/1969.1/149343

► This research presents new physics-based methods to estimate predictive uncertainty stemming from uncertainty in the material opacities in radiative transfer computations of key quantities of… (more)

Subjects/Keywords: Uncertainty Quantification; Sensitivity Analysis

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APA (6^th Edition):

Hetzler, A. C. (2013). Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/149343

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hetzler, Adam C. “Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem.” 2013. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/149343.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hetzler, Adam C. “Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem.” 2013. Web. 16 Oct 2019.

Vancouver:

Hetzler AC. Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/149343.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hetzler AC. Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/149343

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

26. Stripling, Hayes Franklin. Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations.

Degree: 2013, Texas A&M University

URL: http://hdl.handle.net/1969.1/151312

► Depletion calculations for nuclear reactors model the dynamic coupling between the material composition and neutron flux and help predict reactor performance and safety characteristics. In… (more)

▼ Depletion calculations for nuclear reactors model the dynamic coupling between the material composition and neutron flux and help predict reactor performance and safety characteristics. In order to be trusted as reliable predictive tools and inputs to licensing and operational decisions, the simulations must include an accurate and holistic quantification of errors and uncertainties in its outputs. Uncertainty quantification is a formidable challenge in large, realistic reactor models because of the large number of unknowns and myriad sources of uncertainty and error. We present a framework for performing efficient uncertainty quantification in depletion problems using an adjoint approach, with emphasis on high-fidelity calculations using advanced massively parallel computing architectures. This approach calls for a solution to two systems of equations: (a) the forward, engineering system that models the reactor, and (b) the adjoint system, which is mathematically related to but different from the forward system. We use the solutions of these systems to produce sensitivity and error estimates at a cost that does not grow rapidly with the number of uncertain inputs. We present the framework in a general fashion and apply it to both the source-driven and k-eigenvalue forms of the depletion equations. We describe the implementation and verification of solvers for the forward and ad- joint equations in the PDT code, and we test the algorithms on realistic reactor analysis problems. We demonstrate a new approach for reducing the memory and I/O demands on the host machine, which can be overwhelming for typical adjoint algorithms. Our conclusion is that adjoint depletion calculations using full transport solutions are not only computationally tractable, they are the most attractive option for performing uncertainty quantification on high-fidelity reactor analysis problems. Advisors/Committee Members: Adams, Marvin L. (advisor), Mallick, Bani K. (committee member), McClarren, Ryan G. (committee member), Morel, Jim E. (committee member), Anitescu, Mihai (committee member).

Subjects/Keywords: Adjoint; Sensitivity Analysis; Uncertainty Quantification; Depletion Calculations

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Stripling, H. F. (2013). Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/151312

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Stripling, Hayes Franklin. “Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations.” 2013. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/151312.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Stripling, Hayes Franklin. “Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations.” 2013. Web. 16 Oct 2019.

Vancouver:

Stripling HF. Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/151312.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Stripling HF. Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/151312

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

27. Sarkar, Abhra. Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors.

Degree: 2014, Texas A&M University

URL: http://hdl.handle.net/1969.1/153327

► Although the literature on measurement error problems is quite extensive, solutions to even the most fundamental measurement error problems like density deconvolution and regression with… (more)

▼ Although the literature on measurement error problems is quite extensive, solutions to even the most fundamental measurement error problems like density deconvolution and regression with errors-in-covariates are available only under numerous simplifying and unrealistic assumptions. This dissertation demonstrates that Bayesian methods, by accommodating measurement errors through natural hierarchies, can provide a very powerful framework for solving these important measurement errors problems under more realistic scenarios. However, the very presence of measurement errors often renders techniques that are successful in measurement error free scenarios inefficient, numerically unstable, computationally challenging or intractable. Additionally, measurement error problems often have unique features that compound modeling and computational challenges. In this dissertation, we develop novel Bayesian semiparametric approaches that cater to these unique challenges of measurement error problems and allow us to break free from many restrictive parametric assumptions of previously existing approaches. In this dissertation, we first consider the problem of univariate density deconvolution when replicated proxies are available for each unknown value of the variable of interest. Existing deconvolution methods often make restrictive and unrealistic assumptions about the density of interest and the distribution of measurement errors, e.g., normality and homoscedasticity and thus independence from the variable of interest. We relax these assumptions and develop robust and efficient deconvolution approaches based on Dirichlet process mixture models and mixtures of B-splines in the presence of conditionally heteroscedastic measurement errors. We then extend the methodology to nonlinear univariate regression with errors-in-covariates problems when the densities of the covariate, the regression errors and the measurement errors are all unknown, and the regression and the measurement errors are conditionally heteroscedastic. The final section of this dissertation is devoted to the development of flexible multivariate density deconvolution approaches. The methods available in the existing sparse literature all assume the measurement error density to be fully specified. In contrast, we develop multivariate deconvolution approaches for scenarios when the measurement error density is unknown but replicated proxies are available for each subject. We consider scenarios when the measurement errors are distributed independently from the vector valued variable of interest as well as scenarios when they are conditionally heteroscedastic. To meet the significantly harder modeling and computational challenges of the multivariate problem, we exploit properties of finite mixture models, multivariate normal kernels, latent factor models and exchangeable priors in many novel ways. We provide theoretical results showing the flexibility of the proposed models. In simulation experiments, the proposed semiparametric methods vastly outperform previously… Advisors/Committee Members: Mallick, Bani K. (advisor), Carroll, Raymond J. (advisor), Bhattacharya, Anirban (committee member), Yoon, Byung-Jun (committee member).

Subjects/Keywords: B-splines; Conditional heteroscedasticity; Density deconvolution; Dirichlet process; Latent factor analyzers; Measurement errors; Mixture models; Nutritional epidemiology; Regression with errors in covariates; Sparsity inducing priors

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sarkar, A. (2014). Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153327

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sarkar, Abhra. “Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors.” 2014. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/153327.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sarkar, Abhra. “Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors.” 2014. Web. 16 Oct 2019.

Vancouver:

Sarkar A. Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/153327.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sarkar A. Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153327

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

28. Goddard, Scott D. Restricted Most Powerful Bayesian Tests.

Degree: 2015, Texas A&M University

URL: http://hdl.handle.net/1969.1/155108

► Uniformly most powerful Bayesian tests (UMPBTs) are defined to be Bayesian tests that maximize the probability that the Bayes factor against a fixed null hypothesis… (more)

Subjects/Keywords: Hypothesis tests; g prior; UMPBT; Bayesian variable selection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Goddard, S. D. (2015). Restricted Most Powerful Bayesian Tests. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/155108

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Goddard, Scott D. “Restricted Most Powerful Bayesian Tests.” 2015. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/155108.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Goddard, Scott D. “Restricted Most Powerful Bayesian Tests.” 2015. Web. 16 Oct 2019.

Vancouver:

Goddard SD. Restricted Most Powerful Bayesian Tests. [Internet] [Thesis]. Texas A&M University; 2015. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/155108.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Goddard SD. Restricted Most Powerful Bayesian Tests. [Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/155108

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

29. Alahmadi, Hasan Ali H. A Model for Optimizing Energy Investments and Policy Under Uncertainty with Application to Saudi Arabia.

Degree: PhD, Petroleum Engineering, 2016, Texas A&M University

URL: http://hdl.handle.net/1969.1/157993

► An energy producer must determine optimal energy investment strategies in order to maximize the value of its energy portfolio. Determining optimal investment strategies is challenging.… (more)

▼ An energy producer must determine optimal energy investment strategies in order to maximize the value of its energy portfolio. Determining optimal investment strategies is challenging. One of the main challenges is the large uncertainty in many of the parameters involved in the optimization process. Existing large-scale energy models are mostly deterministic and thus have limited capability for assessing uncertainty. Modelers usually use scenario analysis to address model input uncertainty. In this research, I developed a probabilistic model for optimizing energy investments and policies from an energy producer’s perspective. The model uses a top-down approach to probabilistically forecast primary energy demand. Distributions rather than static values are used to model uncertainty in the input variables. The model can be applied to a country-level energy system. It maximizes the portfolio expected net present value (ENPV) while ensuring energy sustainability. The model was built in MSExcel® using the @RISK Palisade add-in, which is capable of modeling uncertain parameters and performing stochastic simulation optimization. The model was applied to Saudi Arabia to determine its optimum energy investment strategy, determine the value of investing in alternative energy sources, and compare deterministic and probabilistic modeling approaches. The model, given its assumptions and limitations, suggests that Saudi Arabia should keep its oil production capacity at 12.5 million barrels per day, especially in the short term. It also suggests that most of the future power-generation (electricity) demand in Saudi Arabia should be met using alternative-energy sources (nuclear, solar, and wind). Otherwise, large gas production is required to meet such demand. In addition, comparing probabilistic to deterministic model results shows that deterministic models may overestimate total portfolio ENPV and underestimate future investments needed to meet projected power demand. A primary contribution of this work is rigorously addressing uncertainty quantification in energy modeling. Building probabilistic energy models is one of the challenges facing the industry today. The model is also the first, to the best of my knowledge, that attempts to optimize Saudi Arabia’s energy portfolio using a probabilistic approach and addressing the value of investing in alternative energy sources. Advisors/Committee Members: McVay, Duane A (advisor), Gildin, Eduardo (committee member), Voneiff, George W (committee member), Mallick, Bani K (committee member).

Subjects/Keywords: Energy Optimization; Uncertainty Quantification; Probabilistic Energy Modeling; Energy Economics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Alahmadi, H. A. H. (2016). A Model for Optimizing Energy Investments and Policy Under Uncertainty with Application to Saudi Arabia. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/157993

Chicago Manual of Style (16^th Edition):

Alahmadi, Hasan Ali H. “A Model for Optimizing Energy Investments and Policy Under Uncertainty with Application to Saudi Arabia.” 2016. Doctoral Dissertation, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/157993.

MLA Handbook (7^th Edition):

Alahmadi, Hasan Ali H. “A Model for Optimizing Energy Investments and Policy Under Uncertainty with Application to Saudi Arabia.” 2016. Web. 16 Oct 2019.

Vancouver:

Alahmadi HAH. A Model for Optimizing Energy Investments and Policy Under Uncertainty with Application to Saudi Arabia. [Internet] [Doctoral dissertation]. Texas A&M University; 2016. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/157993.

Council of Science Editors:

Alahmadi HAH. A Model for Optimizing Energy Investments and Policy Under Uncertainty with Application to Saudi Arabia. [Doctoral Dissertation]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/157993

Texas A&M University

30. Maity, Arnab. Efficient inference in general semiparametric regression models.

Degree: 2009, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-3075

► Semiparametric regression has become very popular in the field of Statistics over the years. While on one hand more and more sophisticated models are being… (more)

▼ Semiparametric regression has become very popular in the field of Statistics over the years. While on one hand more and more sophisticated models are being developed, on the other hand the resulting theory and estimation process has become more and more involved. The main problems that are addressed in this work are related to efficient inferential procedures in general semiparametric regression problems. We first discuss efficient estimation of population-level summaries in general semiparametric regression models. Here our focus is on estimating general population-level quantities that combine the parametric and nonparametric parts of the model (e.g., population mean, probabilities, etc.). We place this problem in a general context, provide a general kernel-based methodology, and derive the asymptotic distributions of estimates of these population-level quantities, showing that in many cases the estimates are semiparametric efficient. Next, motivated from the problem of testing for genetic effects on complex traits in the presence of gene-environment interaction, we consider developing score test in general semiparametric regression problems that involves Tukey style 1 d.f form of interaction between parametrically and non-parametrically modeled covariates. We develop adjusted score statistics which are unbiased and asymptotically efficient and can be performed using standard bandwidth selection methods. In addition, to over come the difficulty of solving functional equations, we give easy interpretations of the target functions, which in turn allow us to develop estimation procedures that can be easily implemented using standard computational methods. Finally, we take up the important problem of estimation in a general semiparametric regression model when covariates are measured with an additive measurement error structure having normally distributed measurement errors. In contrast to methods that require solving integral equation of dimension the size of the covariate measured with error, we propose methodology based on Monte Carlo corrected scores to estimate the model components and investigate the asymptotic behavior of the estimates. For each of the problems, we present simulation studies to observe the performance of the proposed inferential procedures. In addition, we apply our proposed methodology to analyze nontrivial real life data sets and present the results. Advisors/Committee Members: Carroll, Raymond J. (advisor), Dougherty, Edward R. (committee member), Lahiri, Soumendra N. (committee member), Mallick, Bani K. (committee member), Mueller-Harknett, Ursula (committee member).

Subjects/Keywords: Nonparametric/Semiparametric Regression; Kernel Method; Measurement Error; Semiparametric Efficiency; Repeated Measures

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Maity, A. (2009). Efficient inference in general semiparametric regression models. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-3075

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Maity, Arnab. “Efficient inference in general semiparametric regression models.” 2009. Thesis, Texas A&M University. Accessed October 16, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-3075.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Maity, Arnab. “Efficient inference in general semiparametric regression models.” 2009. Web. 16 Oct 2019.

Vancouver:

Maity A. Efficient inference in general semiparametric regression models. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-3075.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Maity A. Efficient inference in general semiparametric regression models. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-3075

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations