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You searched for +publisher:"Texas A&M University" +contributor:("Mallick, Bani"). Showing records 1 – 30 of 77 total matches.

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Texas A&M University

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

Degree: MS, Nuclear Engineering, 2014, Texas A&M University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Peterson, Jacob Ross. “Exponentially-convergent Monte Carlo for the One-dimensional Transport Equation.” 2014. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/152727.

MLA Handbook (7th Edition):

Peterson, Jacob Ross. “Exponentially-convergent Monte Carlo for the One-dimensional Transport Equation.” 2014. Web. 07 Mar 2021.

Vancouver:

Peterson JR. Exponentially-convergent Monte Carlo for the One-dimensional Transport Equation. [Internet] [Masters thesis]. Texas A&M University; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/152727.

Council of Science Editors:

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


Texas A&M University

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

Degree: PhD, Statistics, 2017, Texas A&M University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Dorn, Mary Frances. “Semiparametric Classification under a Forest Density Assumption.” 2017. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/161361.

MLA Handbook (7th Edition):

Dorn, Mary Frances. “Semiparametric Classification under a Forest Density Assumption.” 2017. Web. 07 Mar 2021.

Vancouver:

Dorn MF. Semiparametric Classification under a Forest Density Assumption. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/161361.

Council of Science Editors:

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


Texas A&M University

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

Degree: PhD, Statistics, 2012, Texas A&M University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Konomi, Bledar. “Bayesian Spatial Modeling of Complex and High Dimensional Data.” 2012. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10267.

MLA Handbook (7th Edition):

Konomi, Bledar. “Bayesian Spatial Modeling of Complex and High Dimensional Data.” 2012. Web. 07 Mar 2021.

Vancouver:

Konomi B. Bayesian Spatial Modeling of Complex and High Dimensional Data. [Internet] [Doctoral dissertation]. Texas A&M University; 2012. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10267.

Council of Science Editors:

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


Texas A&M University

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

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

 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)

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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Aldossary, Mubarak Nasser. “The Value of Assessing Uncertainty.” 2016. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/156856.

MLA Handbook (7th Edition):

Aldossary, Mubarak Nasser. “The Value of Assessing Uncertainty.” 2016. Web. 07 Mar 2021.

Vancouver:

Aldossary MN. The Value of Assessing Uncertainty. [Internet] [Doctoral dissertation]. Texas A&M University; 2016. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/156856.

Council of Science Editors:

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


Texas A&M University

5. Li, Furong. Statistical Inference for Large Spatial Data.

Degree: PhD, Statistics, 2017, Texas A&M University

 The availability of large spatial and spatial-temporal data geocoded at accurate locations has fueled increasing interest in spatial modeling and analysis. In this dissertation, we… (more)

Subjects/Keywords: Weighted composite likelihood; Spatially clustered coefficient regression; Penalized spectral regression.

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APA (6th Edition):

Li, F. (2017). Statistical Inference for Large Spatial Data. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/174900

Chicago Manual of Style (16th Edition):

Li, Furong. “Statistical Inference for Large Spatial Data.” 2017. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/174900.

MLA Handbook (7th Edition):

Li, Furong. “Statistical Inference for Large Spatial Data.” 2017. Web. 07 Mar 2021.

Vancouver:

Li F. Statistical Inference for Large Spatial Data. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/174900.

Council of Science Editors:

Li F. Statistical Inference for Large Spatial Data. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/174900


Texas A&M University

6. Payne, Richard Daniel. Two-Stage Metropolis Hastings; Bayesian Conditional Density Estimation & Survival Analysis via Partition Modeling, Laplace Approximations, and Efficient Computation.

Degree: PhD, Statistics, 2018, Texas A&M University

 Bayesian statistical methods are known for their flexibility in modeling. This flexibility is possible because parameters can often be estimated via Markov chain Monte Carlo… (more)

Subjects/Keywords: Bayesian statistics; Laplace approximation; partition model; Gaussian process; Markov chain Monte Carlo; survival analysis

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APA (6th Edition):

Payne, R. D. (2018). Two-Stage Metropolis Hastings; Bayesian Conditional Density Estimation & Survival Analysis via Partition Modeling, Laplace Approximations, and Efficient Computation. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173405

Chicago Manual of Style (16th Edition):

Payne, Richard Daniel. “Two-Stage Metropolis Hastings; Bayesian Conditional Density Estimation & Survival Analysis via Partition Modeling, Laplace Approximations, and Efficient Computation.” 2018. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/173405.

MLA Handbook (7th Edition):

Payne, Richard Daniel. “Two-Stage Metropolis Hastings; Bayesian Conditional Density Estimation & Survival Analysis via Partition Modeling, Laplace Approximations, and Efficient Computation.” 2018. Web. 07 Mar 2021.

Vancouver:

Payne RD. Two-Stage Metropolis Hastings; Bayesian Conditional Density Estimation & Survival Analysis via Partition Modeling, Laplace Approximations, and Efficient Computation. [Internet] [Doctoral dissertation]. Texas A&M University; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/173405.

Council of Science Editors:

Payne RD. Two-Stage Metropolis Hastings; Bayesian Conditional Density Estimation & Survival Analysis via Partition Modeling, Laplace Approximations, and Efficient Computation. [Doctoral Dissertation]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173405


Texas A&M University

7. Chakraborty, Antik. Bayesian Shrinkage: Computation, Methods and Theory.

Degree: PhD, Statistics, 2018, Texas A&M University

 Sparsity is a standard structural assumption that is made while modeling high-dimensional statistical parameters. This assumption essentially entails a lower dimensional embedding of the high-dimensional… (more)

Subjects/Keywords: High-dimensional; Sparsity; Shrinkage priors; Low-rank; Convergence rates; Factor models; Regression

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APA (6th Edition):

Chakraborty, A. (2018). Bayesian Shrinkage: Computation, Methods and Theory. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/174156

Chicago Manual of Style (16th Edition):

Chakraborty, Antik. “Bayesian Shrinkage: Computation, Methods and Theory.” 2018. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/174156.

MLA Handbook (7th Edition):

Chakraborty, Antik. “Bayesian Shrinkage: Computation, Methods and Theory.” 2018. Web. 07 Mar 2021.

Vancouver:

Chakraborty A. Bayesian Shrinkage: Computation, Methods and Theory. [Internet] [Doctoral dissertation]. Texas A&M University; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/174156.

Council of Science Editors:

Chakraborty A. Bayesian Shrinkage: Computation, Methods and Theory. [Doctoral Dissertation]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/174156


Texas A&M University

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

Degree: PhD, Statistics, 2014, Texas A&M University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

De, Debkumar. “Essays on Bayesian Time Series and Variable Selection.” 2014. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/152793.

MLA Handbook (7th Edition):

De, Debkumar. “Essays on Bayesian Time Series and Variable Selection.” 2014. Web. 07 Mar 2021.

Vancouver:

De D. Essays on Bayesian Time Series and Variable Selection. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/152793.

Council of Science Editors:

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


Texas A&M University

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

Degree: PhD, Statistics, 2014, Texas A&M University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Roh, Soojin. “Robust Ensemble Kalman Filters and Localization for Multiple State Variables.” 2014. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/153268.

MLA Handbook (7th Edition):

Roh, Soojin. “Robust Ensemble Kalman Filters and Localization for Multiple State Variables.” 2014. Web. 07 Mar 2021.

Vancouver:

Roh S. Robust Ensemble Kalman Filters and Localization for Multiple State Variables. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/153268.

Council of Science Editors:

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


Texas A&M University

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

Degree: PhD, Statistics, 2014, Texas A&M University

 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)

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Wang, Yanqing. “Relative Risks Analysis in Nutritional Epidemiology.” 2014. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/153663.

MLA Handbook (7th Edition):

Wang, Yanqing. “Relative Risks Analysis in Nutritional Epidemiology.” 2014. Web. 07 Mar 2021.

Vancouver:

Wang Y. Relative Risks Analysis in Nutritional Epidemiology. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/153663.

Council of Science Editors:

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


Texas A&M University

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

Degree: MS, Petroleum Engineering, 2014, Texas A&M University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Xia, Xiaoyang. “History-Matching Production Data Using Ensemble Smoother with Multiple Data Assimilation: A Comparative Study.” 2014. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/154226.

MLA Handbook (7th Edition):

Xia, Xiaoyang. “History-Matching Production Data Using Ensemble Smoother with Multiple Data Assimilation: A Comparative Study.” 2014. Web. 07 Mar 2021.

Vancouver:

Xia X. History-Matching Production Data Using Ensemble Smoother with Multiple Data Assimilation: A Comparative Study. [Internet] [Masters thesis]. Texas A&M University; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/154226.

Council of Science Editors:

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


Texas A&M University

12. Xue, Jingnan. Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion.

Degree: PhD, Statistics, 2017, Texas A&M University

 Big data analysis and high dimensional data analysis are two popular and challenging topics in current statistical research. They bring us a lot of opportunities… (more)

Subjects/Keywords: Variable selection; variable screening; ultrahigh dimensional data analysis; big data; parallel computing; MCMC

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APA (6th Edition):

Xue, J. (2017). Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/187253

Chicago Manual of Style (16th Edition):

Xue, Jingnan. “Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion.” 2017. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/187253.

MLA Handbook (7th Edition):

Xue, Jingnan. “Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion.” 2017. Web. 07 Mar 2021.

Vancouver:

Xue J. Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/187253.

Council of Science Editors:

Xue J. Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/187253


Texas A&M University

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

Degree: PhD, Statistics, 2012, Texas A&M University

 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)

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Zhang, Lin. “Application of Bayesian Hierarchical Models in Genetic Data Analysis.” 2012. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/148056.

MLA Handbook (7th Edition):

Zhang, Lin. “Application of Bayesian Hierarchical Models in Genetic Data Analysis.” 2012. Web. 07 Mar 2021.

Vancouver:

Zhang L. Application of Bayesian Hierarchical Models in Genetic Data Analysis. [Internet] [Doctoral dissertation]. Texas A&M University; 2012. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/148056.

Council of Science Editors:

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


Texas A&M University

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

Degree: MS, Petroleum Engineering, 2013, Texas A&M University

 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)

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Olalotiti-Lawal, Feyisayo. “Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification.” 2013. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/150964.

MLA Handbook (7th Edition):

Olalotiti-Lawal, Feyisayo. “Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification.” 2013. Web. 07 Mar 2021.

Vancouver:

Olalotiti-Lawal F. Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification. [Internet] [Masters thesis]. Texas A&M University; 2013. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/150964.

Council of Science Editors:

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


Texas A&M University

15. Mandal, Soutrik. Analysis and Goodness-of-Fit Tests for Time-to-Event Models.

Degree: PhD, Statistics, 2018, Texas A&M University

 The Cox proportional hazards model and the proportional odds model are some of the popular survival models often chosen to analyze censored time-to-event data. The… (more)

Subjects/Keywords: interval censoring; linear transformation models; multiple imputation; semiparametric methods; martingale; goodness-of-fit tests

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APA (6th Edition):

Mandal, S. (2018). Analysis and Goodness-of-Fit Tests for Time-to-Event Models. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/174133

Chicago Manual of Style (16th Edition):

Mandal, Soutrik. “Analysis and Goodness-of-Fit Tests for Time-to-Event Models.” 2018. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/174133.

MLA Handbook (7th Edition):

Mandal, Soutrik. “Analysis and Goodness-of-Fit Tests for Time-to-Event Models.” 2018. Web. 07 Mar 2021.

Vancouver:

Mandal S. Analysis and Goodness-of-Fit Tests for Time-to-Event Models. [Internet] [Doctoral dissertation]. Texas A&M University; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/174133.

Council of Science Editors:

Mandal S. Analysis and Goodness-of-Fit Tests for Time-to-Event Models. [Doctoral Dissertation]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/174133


Texas A&M University

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

Degree: PhD, Statistics, 2012, Texas A&M University

 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)

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Xun, Xiaolei. “Statistical Inference in Inverse Problems.” 2012. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10874.

MLA Handbook (7th Edition):

Xun, Xiaolei. “Statistical Inference in Inverse Problems.” 2012. Web. 07 Mar 2021.

Vancouver:

Xun X. Statistical Inference in Inverse Problems. [Internet] [Doctoral dissertation]. Texas A&M University; 2012. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10874.

Council of Science Editors:

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


Texas A&M University

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

Degree: MS, Petroleum Engineering, 2011, Texas A&M University

 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)

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Tao, Qing. “A Comparison of Waterflood Management Using Arrival Time Optimization and NPV Optimization.” 2011. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7238.

MLA Handbook (7th Edition):

Tao, Qing. “A Comparison of Waterflood Management Using Arrival Time Optimization and NPV Optimization.” 2011. Web. 07 Mar 2021.

Vancouver:

Tao Q. A Comparison of Waterflood Management Using Arrival Time Optimization and NPV Optimization. [Internet] [Masters thesis]. Texas A&M University; 2011. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7238.

Council of Science Editors:

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


Texas A&M University

18. Liu, Senmao. A Comprehensive Approach for Sparse Principle Component Analysis using Regularized Singular Value Decomposition.

Degree: PhD, Statistics, 2016, Texas A&M University

 Principle component analysis (PCA) has been a widely used tool for statistics and data analysis for many years. A good result of PCA should be… (more)

Subjects/Keywords: Principal Component Analysis; Sparse PCA; Singular Value Decomposition; Regularized SVD; Alternating Direction; Block Coordinate Descent; Regularity; Power Iteration; Global Optima; Orthogonal Constraint; Missing Values; Cross-Validation.

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APA (6th Edition):

Liu, S. (2016). A Comprehensive Approach for Sparse Principle Component Analysis using Regularized Singular Value Decomposition. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/192022

Chicago Manual of Style (16th Edition):

Liu, Senmao. “A Comprehensive Approach for Sparse Principle Component Analysis using Regularized Singular Value Decomposition.” 2016. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/192022.

MLA Handbook (7th Edition):

Liu, Senmao. “A Comprehensive Approach for Sparse Principle Component Analysis using Regularized Singular Value Decomposition.” 2016. Web. 07 Mar 2021.

Vancouver:

Liu S. A Comprehensive Approach for Sparse Principle Component Analysis using Regularized Singular Value Decomposition. [Internet] [Doctoral dissertation]. Texas A&M University; 2016. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/192022.

Council of Science Editors:

Liu S. A Comprehensive Approach for Sparse Principle Component Analysis using Regularized Singular Value Decomposition. [Doctoral Dissertation]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/192022


Texas A&M University

19. Larsen, Allyson Elaine. Approximation Schemes to Simplify Posterior Computation.

Degree: PhD, Statistics, 2020, Texas A&M University

 Markov chain Monte Carlo (MCMC) sampling methods often do not scale well to large datasets, so there has been an increased interest in approximate Markov… (more)

Subjects/Keywords: Approximate; Markov chain Monte Carlo

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APA (6th Edition):

Larsen, A. E. (2020). Approximation Schemes to Simplify Posterior Computation. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/192454

Chicago Manual of Style (16th Edition):

Larsen, Allyson Elaine. “Approximation Schemes to Simplify Posterior Computation.” 2020. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/192454.

MLA Handbook (7th Edition):

Larsen, Allyson Elaine. “Approximation Schemes to Simplify Posterior Computation.” 2020. Web. 07 Mar 2021.

Vancouver:

Larsen AE. Approximation Schemes to Simplify Posterior Computation. [Internet] [Doctoral dissertation]. Texas A&M University; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/192454.

Council of Science Editors:

Larsen AE. Approximation Schemes to Simplify Posterior Computation. [Doctoral Dissertation]. Texas A&M University; 2020. Available from: http://hdl.handle.net/1969.1/192454


Texas A&M University

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

Degree: PhD, Nuclear Engineering, 2013, Texas A&M University

 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)

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Stripling, Hayes Franklin. “Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations.” 2013. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/151312.

MLA Handbook (7th Edition):

Stripling, Hayes Franklin. “Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations.” 2013. Web. 07 Mar 2021.

Vancouver:

Stripling HF. Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations. [Internet] [Doctoral dissertation]. Texas A&M University; 2013. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/151312.

Council of Science Editors:

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


Texas A&M University

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

Degree: PhD, Statistics, 2014, Texas A&M University

 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)

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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Wei, Rubin. “Highly Nonlinear Measurement Error Models in Nutritional Epidemiology.” 2014. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/161236.

MLA Handbook (7th Edition):

Wei, Rubin. “Highly Nonlinear Measurement Error Models in Nutritional Epidemiology.” 2014. Web. 07 Mar 2021.

Vancouver:

Wei R. Highly Nonlinear Measurement Error Models in Nutritional Epidemiology. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/161236.

Council of Science Editors:

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


Texas A&M University

22. Olalotiti-Lawal, Feyisayo Omoniyi. Effective Reservoir Management for Carbon Utilization and Storage Applications.

Degree: PhD, Petroleum Engineering, 2018, Texas A&M University

 It is believed that the observed rapid rise in global temperatures is caused by high atmospheric concentration of CO2, due to emissions from fossil fuel… (more)

Subjects/Keywords: CCUS; CO2 EOR; CO2 Storage; Subsurface Model Reparameterization; Production Optimization

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APA (6th Edition):

Olalotiti-Lawal, F. O. (2018). Effective Reservoir Management for Carbon Utilization and Storage Applications. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173427

Chicago Manual of Style (16th Edition):

Olalotiti-Lawal, Feyisayo Omoniyi. “Effective Reservoir Management for Carbon Utilization and Storage Applications.” 2018. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/173427.

MLA Handbook (7th Edition):

Olalotiti-Lawal, Feyisayo Omoniyi. “Effective Reservoir Management for Carbon Utilization and Storage Applications.” 2018. Web. 07 Mar 2021.

Vancouver:

Olalotiti-Lawal FO. Effective Reservoir Management for Carbon Utilization and Storage Applications. [Internet] [Doctoral dissertation]. Texas A&M University; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/173427.

Council of Science Editors:

Olalotiti-Lawal FO. Effective Reservoir Management for Carbon Utilization and Storage Applications. [Doctoral Dissertation]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173427


Texas A&M University

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

Degree: PhD, Statistics, 2014, Texas A&M University

 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)

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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Sarkar, Abhra. “Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors.” 2014. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/153327.

MLA Handbook (7th Edition):

Sarkar, Abhra. “Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors.” 2014. Web. 07 Mar 2021.

Vancouver:

Sarkar A. Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/153327.

Council of Science Editors:

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


Texas A&M University

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

Degree: PhD, Statistics, 2015, Texas A&M University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Goddard, Scott D. “Restricted Most Powerful Bayesian Tests.” 2015. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/155108.

MLA Handbook (7th Edition):

Goddard, Scott D. “Restricted Most Powerful Bayesian Tests.” 2015. Web. 07 Mar 2021.

Vancouver:

Goddard SD. Restricted Most Powerful Bayesian Tests. [Internet] [Doctoral dissertation]. Texas A&M University; 2015. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/155108.

Council of Science Editors:

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


Texas A&M University

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

Degree: PhD, Nuclear Engineering, 2013, Texas A&M University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Hetzler, Adam C. “Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem.” 2013. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/149343.

MLA Handbook (7th Edition):

Hetzler, Adam C. “Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem.” 2013. Web. 07 Mar 2021.

Vancouver:

Hetzler AC. Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem. [Internet] [Doctoral dissertation]. Texas A&M University; 2013. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/149343.

Council of Science Editors:

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


Texas A&M University

26. 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

 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)

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

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APA (6th 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 (16th 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 March 07, 2021. http://hdl.handle.net/1969.1/157993.

MLA Handbook (7th Edition):

Alahmadi, Hasan Ali H. “A Model for Optimizing Energy Investments and Policy Under Uncertainty with Application to Saudi Arabia.” 2016. Web. 07 Mar 2021.

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 2021 Mar 07]. 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

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

Degree: PhD, Statistics, 2012, Texas A&M University

 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)

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Talluri, Rajesh. “Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.” 2012. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828.

MLA Handbook (7th Edition):

Talluri, Rajesh. “Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.” 2012. Web. 07 Mar 2021.

Vancouver:

Talluri R. Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. [Internet] [Doctoral dissertation]. Texas A&M University; 2012. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828.

Council of Science Editors:

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


Texas A&M University

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

Degree: PhD, Statistics, 2015, Texas A&M University

 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)

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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

Rahman, Shahina. “Efficient Nonparametric and Semiparametric Regression Methods with application in Case-Control Studies.” 2015. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/155719.

MLA Handbook (7th Edition):

Rahman, Shahina. “Efficient Nonparametric and Semiparametric Regression Methods with application in Case-Control Studies.” 2015. Web. 07 Mar 2021.

Vancouver:

Rahman S. Efficient Nonparametric and Semiparametric Regression Methods with application in Case-Control Studies. [Internet] [Doctoral dissertation]. Texas A&M University; 2015. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/155719.

Council of Science Editors:

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


Texas A&M University

29. Vyas, Aditya. Application of Machine Learning in Well Performance Prediction, Design Optimization and History Matching.

Degree: PhD, Petroleum Engineering, 2017, Texas A&M University

 Finite difference based reservoir simulation is commonly used to predict well rates in these reservoirs. Such detailed simulation requires an accurate knowledge of reservoir geology.… (more)

Subjects/Keywords: Unconventional Reservoirs; Machine Learning; Data Analytics; Decline Curves; Hydraulic Fracture Optimization; History Matching

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APA (6th Edition):

Vyas, A. (2017). Application of Machine Learning in Well Performance Prediction, Design Optimization and History Matching. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/187248

Chicago Manual of Style (16th Edition):

Vyas, Aditya. “Application of Machine Learning in Well Performance Prediction, Design Optimization and History Matching.” 2017. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/187248.

MLA Handbook (7th Edition):

Vyas, Aditya. “Application of Machine Learning in Well Performance Prediction, Design Optimization and History Matching.” 2017. Web. 07 Mar 2021.

Vancouver:

Vyas A. Application of Machine Learning in Well Performance Prediction, Design Optimization and History Matching. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/187248.

Council of Science Editors:

Vyas A. Application of Machine Learning in Well Performance Prediction, Design Optimization and History Matching. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/187248

30. Stripling, Hayes Franklin. The Method of Manufactured Universes for Testing Uncertainty Quantification Methods.

Degree: MS, Nuclear Engineering, 2011, Texas A&M University

 The Method of Manufactured Universes is presented as a validation framework for uncertainty quantification (UQ) methodologies and as a tool for exploring the effects of… (more)

Subjects/Keywords: Uncertainty Quantification; Validation; Bayesian Inversion; Calibration

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APA (6th Edition):

Stripling, H. F. (2011). The Method of Manufactured Universes for Testing Uncertainty Quantification Methods. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8986

Chicago Manual of Style (16th Edition):

Stripling, Hayes Franklin. “The Method of Manufactured Universes for Testing Uncertainty Quantification Methods.” 2011. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8986.

MLA Handbook (7th Edition):

Stripling, Hayes Franklin. “The Method of Manufactured Universes for Testing Uncertainty Quantification Methods.” 2011. Web. 07 Mar 2021.

Vancouver:

Stripling HF. The Method of Manufactured Universes for Testing Uncertainty Quantification Methods. [Internet] [Masters thesis]. Texas A&M University; 2011. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8986.

Council of Science Editors:

Stripling HF. The Method of Manufactured Universes for Testing Uncertainty Quantification Methods. [Masters Thesis]. Texas A&M University; 2011. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8986

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