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You searched for subject:(Bayesian model selection). Showing records 1 – 30 of 88 total matches.

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University of Kansas

1. Yang, Lei. Parametric and Non-Parametric Models in Health Research: Analysis and Design.

Degree: PhD, Biostatistics, 2016, University of Kansas

 Personalized medicine is emerging in both clinical practice and clinical trials. “Precision” medicine not only promises improved safety and efficacy but also lowered cost in… (more)

Subjects/Keywords: Biostatistics; Bayesian; CART; Model selection

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APA (6th Edition):

Yang, L. (2016). Parametric and Non-Parametric Models in Health Research: Analysis and Design. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/24821

Chicago Manual of Style (16th Edition):

Yang, Lei. “Parametric and Non-Parametric Models in Health Research: Analysis and Design.” 2016. Doctoral Dissertation, University of Kansas. Accessed February 25, 2020. http://hdl.handle.net/1808/24821.

MLA Handbook (7th Edition):

Yang, Lei. “Parametric and Non-Parametric Models in Health Research: Analysis and Design.” 2016. Web. 25 Feb 2020.

Vancouver:

Yang L. Parametric and Non-Parametric Models in Health Research: Analysis and Design. [Internet] [Doctoral dissertation]. University of Kansas; 2016. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/1808/24821.

Council of Science Editors:

Yang L. Parametric and Non-Parametric Models in Health Research: Analysis and Design. [Doctoral Dissertation]. University of Kansas; 2016. Available from: http://hdl.handle.net/1808/24821


University of Rochester

2. LaCombe, Jason R. Non-Informative Priors for Structural Inference in Bayesian Networks.

Degree: PhD, 2011, University of Rochester

 A fundamental problem in multivariate statistics is the determination of dependency relationships among random variables. Bayesian networks equate the dependency properties of the considered variables… (more)

Subjects/Keywords: Bayesian-Networks; Model-Selection; Prior Graphical Model

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APA (6th Edition):

LaCombe, J. R. (2011). Non-Informative Priors for Structural Inference in Bayesian Networks. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/15981

Chicago Manual of Style (16th Edition):

LaCombe, Jason R. “Non-Informative Priors for Structural Inference in Bayesian Networks.” 2011. Doctoral Dissertation, University of Rochester. Accessed February 25, 2020. http://hdl.handle.net/1802/15981.

MLA Handbook (7th Edition):

LaCombe, Jason R. “Non-Informative Priors for Structural Inference in Bayesian Networks.” 2011. Web. 25 Feb 2020.

Vancouver:

LaCombe JR. Non-Informative Priors for Structural Inference in Bayesian Networks. [Internet] [Doctoral dissertation]. University of Rochester; 2011. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/1802/15981.

Council of Science Editors:

LaCombe JR. Non-Informative Priors for Structural Inference in Bayesian Networks. [Doctoral Dissertation]. University of Rochester; 2011. Available from: http://hdl.handle.net/1802/15981


University of Saskatchewan

3. Dong, Yue. A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models.

Degree: 2016, University of Saskatchewan

 When modelling count data, it is possible to have excessive zeros in the data in many applications. My thesis concentrates on the variable selection in… (more)

Subjects/Keywords: Variable selection; Zero-inflated model; Bayesian LASSO

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APA (6th Edition):

Dong, Y. (2016). A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/7313

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

Chicago Manual of Style (16th Edition):

Dong, Yue. “A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models.” 2016. Thesis, University of Saskatchewan. Accessed February 25, 2020. http://hdl.handle.net/10388/7313.

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

MLA Handbook (7th Edition):

Dong, Yue. “A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models.” 2016. Web. 25 Feb 2020.

Vancouver:

Dong Y. A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models. [Internet] [Thesis]. University of Saskatchewan; 2016. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10388/7313.

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

Council of Science Editors:

Dong Y. A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models. [Thesis]. University of Saskatchewan; 2016. Available from: http://hdl.handle.net/10388/7313

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


University of Cambridge

4. Ahfock, Daniel Christian. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.

Degree: PhD, 2019, University of Cambridge

 This thesis is focused on the development of computationally efficient procedures for regression modelling with datasets containing a large number of observations. Standard algorithms be… (more)

Subjects/Keywords: Bayesian model selection; Random projection; Big Data

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APA (6th Edition):

Ahfock, D. C. (2019). New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/291805

Chicago Manual of Style (16th Edition):

Ahfock, Daniel Christian. “New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.” 2019. Doctoral Dissertation, University of Cambridge. Accessed February 25, 2020. https://www.repository.cam.ac.uk/handle/1810/291805.

MLA Handbook (7th Edition):

Ahfock, Daniel Christian. “New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.” 2019. Web. 25 Feb 2020.

Vancouver:

Ahfock DC. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2020 Feb 25]. Available from: https://www.repository.cam.ac.uk/handle/1810/291805.

Council of Science Editors:

Ahfock DC. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/291805


Virginia Tech

5. Metzger, Thomas Anthony. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.

Degree: PhD, Statistics, 2019, Virginia Tech

 Statistical models are a powerful tool for describing a broad range of phenomena in our world. However, many common statistical models may make assumptions that… (more)

Subjects/Keywords: model selection; heteroscedasticity; linear models; Bayesian

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

APA (6th Edition):

Metzger, T. A. (2019). Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/93226

Chicago Manual of Style (16th Edition):

Metzger, Thomas Anthony. “Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.” 2019. Doctoral Dissertation, Virginia Tech. Accessed February 25, 2020. http://hdl.handle.net/10919/93226.

MLA Handbook (7th Edition):

Metzger, Thomas Anthony. “Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.” 2019. Web. 25 Feb 2020.

Vancouver:

Metzger TA. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10919/93226.

Council of Science Editors:

Metzger TA. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/93226


Virginia Tech

6. Maiti, Dipayan. Multiset Model Selection and Averaging, and Interactive Storytelling.

Degree: PhD, Statistics, 2012, Virginia Tech

 The Multiset Sampler [Leman et al., 2009] has previously been deployed and developed for efficient sampling from complex stochastic processes. We extend the sampler and… (more)

Subjects/Keywords: supervised topic modeling; visual analytics; bayesian model averaging; Bayesian mode selection

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APA (6th Edition):

Maiti, D. (2012). Multiset Model Selection and Averaging, and Interactive Storytelling. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28563

Chicago Manual of Style (16th Edition):

Maiti, Dipayan. “Multiset Model Selection and Averaging, and Interactive Storytelling.” 2012. Doctoral Dissertation, Virginia Tech. Accessed February 25, 2020. http://hdl.handle.net/10919/28563.

MLA Handbook (7th Edition):

Maiti, Dipayan. “Multiset Model Selection and Averaging, and Interactive Storytelling.” 2012. Web. 25 Feb 2020.

Vancouver:

Maiti D. Multiset Model Selection and Averaging, and Interactive Storytelling. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10919/28563.

Council of Science Editors:

Maiti D. Multiset Model Selection and Averaging, and Interactive Storytelling. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/28563

7. Farrell, Kathryn Anne. Selection, calibration, and validation of coarse-grained models of atomistic systems.

Degree: PhD, Computational Science, Engineering, and Mathematics, 2015, University of Texas – Austin

 This dissertation examines the development of coarse-grained models of atomistic systems for the purpose of predicting target quantities of interest in the presence of uncertainties.… (more)

Subjects/Keywords: Coarse graining; Bayesian inference; Sensitivity; Model plausibility; Model validation; Model selection

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APA (6th Edition):

Farrell, K. A. (2015). Selection, calibration, and validation of coarse-grained models of atomistic systems. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/30528

Chicago Manual of Style (16th Edition):

Farrell, Kathryn Anne. “Selection, calibration, and validation of coarse-grained models of atomistic systems.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed February 25, 2020. http://hdl.handle.net/2152/30528.

MLA Handbook (7th Edition):

Farrell, Kathryn Anne. “Selection, calibration, and validation of coarse-grained models of atomistic systems.” 2015. Web. 25 Feb 2020.

Vancouver:

Farrell KA. Selection, calibration, and validation of coarse-grained models of atomistic systems. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2152/30528.

Council of Science Editors:

Farrell KA. Selection, calibration, and validation of coarse-grained models of atomistic systems. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/30528


The Ohio State University

8. zheng, jiayin. Calibrated Bayes Factor and Bayesian Model Averaging.

Degree: PhD, Statistics, 2018, The Ohio State University

 There is a rich history of work on model selection and averaging in the statisticsliterature. The Bayesian paradigm provides an approach to model selection whichsuccessfully… (more)

Subjects/Keywords: Statistics; model selection, Bayes factor, unit information prior, Bayesian model averaging

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APA (6th Edition):

zheng, j. (2018). Calibrated Bayes Factor and Bayesian Model Averaging. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265

Chicago Manual of Style (16th Edition):

zheng, jiayin. “Calibrated Bayes Factor and Bayesian Model Averaging.” 2018. Doctoral Dissertation, The Ohio State University. Accessed February 25, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265.

MLA Handbook (7th Edition):

zheng, jiayin. “Calibrated Bayes Factor and Bayesian Model Averaging.” 2018. Web. 25 Feb 2020.

Vancouver:

zheng j. Calibrated Bayes Factor and Bayesian Model Averaging. [Internet] [Doctoral dissertation]. The Ohio State University; 2018. [cited 2020 Feb 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265.

Council of Science Editors:

zheng j. Calibrated Bayes Factor and Bayesian Model Averaging. [Doctoral Dissertation]. The Ohio State University; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265


Rice University

9. Wadsworth, W Duncan. Bayesian Methods for the Analysis of Microbiome Data.

Degree: PhD, Engineering, 2016, Rice University

 Bacteria, archaea, viruses, and fungi are present in large numbers both on and inside of our bodies. On average, only one in ten of “our”… (more)

Subjects/Keywords: Bayesian hierarchical model; Data integration; Dirichlet-Multinomial; Microbiome data; Variable selection; Multiple testing; Bayesian nonparametrics

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APA (6th Edition):

Wadsworth, W. D. (2016). Bayesian Methods for the Analysis of Microbiome Data. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/95977

Chicago Manual of Style (16th Edition):

Wadsworth, W Duncan. “Bayesian Methods for the Analysis of Microbiome Data.” 2016. Doctoral Dissertation, Rice University. Accessed February 25, 2020. http://hdl.handle.net/1911/95977.

MLA Handbook (7th Edition):

Wadsworth, W Duncan. “Bayesian Methods for the Analysis of Microbiome Data.” 2016. Web. 25 Feb 2020.

Vancouver:

Wadsworth WD. Bayesian Methods for the Analysis of Microbiome Data. [Internet] [Doctoral dissertation]. Rice University; 2016. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/1911/95977.

Council of Science Editors:

Wadsworth WD. Bayesian Methods for the Analysis of Microbiome Data. [Doctoral Dissertation]. Rice University; 2016. Available from: http://hdl.handle.net/1911/95977


Université Catholique de Louvain

10. Jaeger, Jonathan. Functional estimation in systems defined by differential equations using Bayesian smoothing methods.

Degree: 2012, Université Catholique de Louvain

Ordinary differential equations (ODEs) are widely used to model physical, chemical and biological processes. Currently, the most commonly used estimation procedures rely on nonlinear least… (more)

Subjects/Keywords: Bayesian ODE-penalized B-spline; ODE-model selection; Ordinary differential equations

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APA (6th Edition):

Jaeger, J. (2012). Functional estimation in systems defined by differential equations using Bayesian smoothing methods. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/115164

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

Chicago Manual of Style (16th Edition):

Jaeger, Jonathan. “Functional estimation in systems defined by differential equations using Bayesian smoothing methods.” 2012. Thesis, Université Catholique de Louvain. Accessed February 25, 2020. http://hdl.handle.net/2078.1/115164.

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

MLA Handbook (7th Edition):

Jaeger, Jonathan. “Functional estimation in systems defined by differential equations using Bayesian smoothing methods.” 2012. Web. 25 Feb 2020.

Vancouver:

Jaeger J. Functional estimation in systems defined by differential equations using Bayesian smoothing methods. [Internet] [Thesis]. Université Catholique de Louvain; 2012. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2078.1/115164.

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

Council of Science Editors:

Jaeger J. Functional estimation in systems defined by differential equations using Bayesian smoothing methods. [Thesis]. Université Catholique de Louvain; 2012. Available from: http://hdl.handle.net/2078.1/115164

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


Penn State University

11. Markley, Scott Conrad. Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures.

Degree: MS, Electrical Engineering, 2009, Penn State University

 Multivariate Gaussian mixtures are widely used in science and engineering for density estimation, model-based data clustering, and statistical classification. A difficult problem, of special interest… (more)

Subjects/Keywords: Expectation Maximization; Model Order Selection; Bayesian Information Criterion; Multivariate Gaussian

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APA (6th Edition):

Markley, S. C. (2009). Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/9613

Chicago Manual of Style (16th Edition):

Markley, Scott Conrad. “Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures.” 2009. Masters Thesis, Penn State University. Accessed February 25, 2020. https://etda.libraries.psu.edu/catalog/9613.

MLA Handbook (7th Edition):

Markley, Scott Conrad. “Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures.” 2009. Web. 25 Feb 2020.

Vancouver:

Markley SC. Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures. [Internet] [Masters thesis]. Penn State University; 2009. [cited 2020 Feb 25]. Available from: https://etda.libraries.psu.edu/catalog/9613.

Council of Science Editors:

Markley SC. Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures. [Masters Thesis]. Penn State University; 2009. Available from: https://etda.libraries.psu.edu/catalog/9613


University of Sydney

12. Duchene Garzon, Sebastian. Understanding Evolutionary Rate Variation in Viruses .

Degree: 2014, University of Sydney

 Estimating the rate of molecular evolution over time is crucial for understanding the processes and forces that shape biological diversity. Viruses are particularly interesting because… (more)

Subjects/Keywords: Molecular-clock; rate of evolution; phylogenetics; Bayesian analyses; model selection

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APA (6th Edition):

Duchene Garzon, S. (2014). Understanding Evolutionary Rate Variation in Viruses . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/13482

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

Chicago Manual of Style (16th Edition):

Duchene Garzon, Sebastian. “Understanding Evolutionary Rate Variation in Viruses .” 2014. Thesis, University of Sydney. Accessed February 25, 2020. http://hdl.handle.net/2123/13482.

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

MLA Handbook (7th Edition):

Duchene Garzon, Sebastian. “Understanding Evolutionary Rate Variation in Viruses .” 2014. Web. 25 Feb 2020.

Vancouver:

Duchene Garzon S. Understanding Evolutionary Rate Variation in Viruses . [Internet] [Thesis]. University of Sydney; 2014. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2123/13482.

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

Council of Science Editors:

Duchene Garzon S. Understanding Evolutionary Rate Variation in Viruses . [Thesis]. University of Sydney; 2014. Available from: http://hdl.handle.net/2123/13482

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


Virginia Tech

13. Loftus, Stephen Christopher. On the Use of Grouped Covariate Regression in Oversaturated Models.

Degree: PhD, Statistics, 2015, Virginia Tech

 As data collection techniques improve, oftentimes the number of covariates exceeds the number of observations. When this happens, regression models become oversaturated and, thus, inestimable.… (more)

Subjects/Keywords: Oversaturated model; Big data; Variable selection; Data Analytics; Bayesian methods

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APA (6th Edition):

Loftus, S. C. (2015). On the Use of Grouped Covariate Regression in Oversaturated Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64363

Chicago Manual of Style (16th Edition):

Loftus, Stephen Christopher. “On the Use of Grouped Covariate Regression in Oversaturated Models.” 2015. Doctoral Dissertation, Virginia Tech. Accessed February 25, 2020. http://hdl.handle.net/10919/64363.

MLA Handbook (7th Edition):

Loftus, Stephen Christopher. “On the Use of Grouped Covariate Regression in Oversaturated Models.” 2015. Web. 25 Feb 2020.

Vancouver:

Loftus SC. On the Use of Grouped Covariate Regression in Oversaturated Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10919/64363.

Council of Science Editors:

Loftus SC. On the Use of Grouped Covariate Regression in Oversaturated Models. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64363


University of Houston

14. Ramos Nunez, Aurora Isabel 1975-. INVESTIGATING THE NEURAL CORRELATES OF LANGUAGE SWITCHING IN SPANISH ENGLISH BILINGUALS EMPLOYING EFFECTIVE CONNECTIVITY ANALYSES.

Degree: Psychology, Department of, 2013, University of Houston

 Bilingualism requires individuals to manage their two languages in order to communicate with others. They may voluntarily or involuntarily switch back and forth between their… (more)

Subjects/Keywords: Bilingualism; Cognitive Control; Dynamic Causal Modeling; Bayesian Model Selection; Language Switching

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APA (6th Edition):

Ramos Nunez, A. I. 1. (2013). INVESTIGATING THE NEURAL CORRELATES OF LANGUAGE SWITCHING IN SPANISH ENGLISH BILINGUALS EMPLOYING EFFECTIVE CONNECTIVITY ANALYSES. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/1241

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

Chicago Manual of Style (16th Edition):

Ramos Nunez, Aurora Isabel 1975-. “INVESTIGATING THE NEURAL CORRELATES OF LANGUAGE SWITCHING IN SPANISH ENGLISH BILINGUALS EMPLOYING EFFECTIVE CONNECTIVITY ANALYSES.” 2013. Thesis, University of Houston. Accessed February 25, 2020. http://hdl.handle.net/10657/1241.

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

MLA Handbook (7th Edition):

Ramos Nunez, Aurora Isabel 1975-. “INVESTIGATING THE NEURAL CORRELATES OF LANGUAGE SWITCHING IN SPANISH ENGLISH BILINGUALS EMPLOYING EFFECTIVE CONNECTIVITY ANALYSES.” 2013. Web. 25 Feb 2020.

Vancouver:

Ramos Nunez AI1. INVESTIGATING THE NEURAL CORRELATES OF LANGUAGE SWITCHING IN SPANISH ENGLISH BILINGUALS EMPLOYING EFFECTIVE CONNECTIVITY ANALYSES. [Internet] [Thesis]. University of Houston; 2013. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10657/1241.

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

Council of Science Editors:

Ramos Nunez AI1. INVESTIGATING THE NEURAL CORRELATES OF LANGUAGE SWITCHING IN SPANISH ENGLISH BILINGUALS EMPLOYING EFFECTIVE CONNECTIVITY ANALYSES. [Thesis]. University of Houston; 2013. Available from: http://hdl.handle.net/10657/1241

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


Northeastern University

15. Guan, Yue. Bayesian models for unsupervised feature selection.

Degree: PhD, Department of Electrical and Computer Engineering, 2012, Northeastern University

 This dissertation focuses on developing probabilistic models for unsupervised feature selection. High-dimensional data often contain irrelevant and redundant features, which can hurt learning algorithms. One… (more)

Subjects/Keywords: Bayesian Model; clustering; Feature selection; PCA; unsupervised learning; Computer Engineering

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APA (6th Edition):

Guan, Y. (2012). Bayesian models for unsupervised feature selection. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/d20002991

Chicago Manual of Style (16th Edition):

Guan, Yue. “Bayesian models for unsupervised feature selection.” 2012. Doctoral Dissertation, Northeastern University. Accessed February 25, 2020. http://hdl.handle.net/2047/d20002991.

MLA Handbook (7th Edition):

Guan, Yue. “Bayesian models for unsupervised feature selection.” 2012. Web. 25 Feb 2020.

Vancouver:

Guan Y. Bayesian models for unsupervised feature selection. [Internet] [Doctoral dissertation]. Northeastern University; 2012. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2047/d20002991.

Council of Science Editors:

Guan Y. Bayesian models for unsupervised feature selection. [Doctoral Dissertation]. Northeastern University; 2012. Available from: http://hdl.handle.net/2047/d20002991


Arizona State University

16. Giacomazzo, Mario. Three Essays on Shrinkage Estimation and Model Selection of Linear and Nonlinear Time Series Models.

Degree: Statistics, 2018, Arizona State University

 The primary objective in time series analysis is forecasting. Raw data often exhibits nonstationary behavior: trends, seasonal cycles, and heteroskedasticity. After data is transformed to… (more)

Subjects/Keywords: Statistics; Bayesian Shrinkage; Model Selection; Nonlinear Time Series

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APA (6th Edition):

Giacomazzo, M. (2018). Three Essays on Shrinkage Estimation and Model Selection of Linear and Nonlinear Time Series Models. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/50104

Chicago Manual of Style (16th Edition):

Giacomazzo, Mario. “Three Essays on Shrinkage Estimation and Model Selection of Linear and Nonlinear Time Series Models.” 2018. Doctoral Dissertation, Arizona State University. Accessed February 25, 2020. http://repository.asu.edu/items/50104.

MLA Handbook (7th Edition):

Giacomazzo, Mario. “Three Essays on Shrinkage Estimation and Model Selection of Linear and Nonlinear Time Series Models.” 2018. Web. 25 Feb 2020.

Vancouver:

Giacomazzo M. Three Essays on Shrinkage Estimation and Model Selection of Linear and Nonlinear Time Series Models. [Internet] [Doctoral dissertation]. Arizona State University; 2018. [cited 2020 Feb 25]. Available from: http://repository.asu.edu/items/50104.

Council of Science Editors:

Giacomazzo M. Three Essays on Shrinkage Estimation and Model Selection of Linear and Nonlinear Time Series Models. [Doctoral Dissertation]. Arizona State University; 2018. Available from: http://repository.asu.edu/items/50104


Harvard University

17. Rischard, Maxime. Considering uncertainty in spatial models: causal inference, missing data imputations, and model comparison.

Degree: PhD, 2019, Harvard University

I present two applications of spatial and spatiotemporal models that stretch the traditional domain of these models, and a method for estimating the marginal likelihood… (more)

Subjects/Keywords: Spatial; spatiotemporal; MCMC; Bayesian; climate; economics; causal inference; regression discontinuity designs; model comparison; model selection

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APA (6th Edition):

Rischard, M. (2019). Considering uncertainty in spatial models: causal inference, missing data imputations, and model comparison. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029482

Chicago Manual of Style (16th Edition):

Rischard, Maxime. “Considering uncertainty in spatial models: causal inference, missing data imputations, and model comparison.” 2019. Doctoral Dissertation, Harvard University. Accessed February 25, 2020. http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029482.

MLA Handbook (7th Edition):

Rischard, Maxime. “Considering uncertainty in spatial models: causal inference, missing data imputations, and model comparison.” 2019. Web. 25 Feb 2020.

Vancouver:

Rischard M. Considering uncertainty in spatial models: causal inference, missing data imputations, and model comparison. [Internet] [Doctoral dissertation]. Harvard University; 2019. [cited 2020 Feb 25]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029482.

Council of Science Editors:

Rischard M. Considering uncertainty in spatial models: causal inference, missing data imputations, and model comparison. [Doctoral Dissertation]. Harvard University; 2019. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029482

18. Shin, Minsuk. Priors for Bayesian Shrinkage and High-Dimensional Model Selection.

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

 This dissertation focuses on the choice of priors in Bayesian model selection and their applied, theoretical and computational aspects. As George Box famously said ?all… (more)

Subjects/Keywords: Bayesian model selection; Nonparametric model

…1.1 1.2 1.3 1 A Brief Review of Bayesian Model Selection… …1 1.1.1 Bayesian Model Selection for the Linear Regression Model . . . . . . . 3 1.1.2… …Bayesian Model Selection in the Nonparametric Regression . . . . . . 5 Research Challenges and… …TABLES 2.1 2.2 Optimal hyperparameters for Bayesian model selection methods… …Brief Review of Bayesian Model Selection Suppose that a set of H models M = {M1… 

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

APA (6th Edition):

Shin, M. (2017). Priors for Bayesian Shrinkage and High-Dimensional Model Selection. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/166096

Chicago Manual of Style (16th Edition):

Shin, Minsuk. “Priors for Bayesian Shrinkage and High-Dimensional Model Selection.” 2017. Doctoral Dissertation, Texas A&M University. Accessed February 25, 2020. http://hdl.handle.net/1969.1/166096.

MLA Handbook (7th Edition):

Shin, Minsuk. “Priors for Bayesian Shrinkage and High-Dimensional Model Selection.” 2017. Web. 25 Feb 2020.

Vancouver:

Shin M. Priors for Bayesian Shrinkage and High-Dimensional Model Selection. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/1969.1/166096.

Council of Science Editors:

Shin M. Priors for Bayesian Shrinkage and High-Dimensional Model Selection. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/166096


Texas A&M University

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

Degree: 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. (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 (16th Edition):

Zhang, Lin. “Application of Bayesian Hierarchical Models in Genetic Data Analysis.” 2012. Thesis, Texas A&M University. Accessed February 25, 2020. 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 (7th Edition):

Zhang, Lin. “Application of Bayesian Hierarchical Models in Genetic Data Analysis.” 2012. Web. 25 Feb 2020.

Vancouver:

Zhang L. Application of Bayesian Hierarchical Models in Genetic Data Analysis. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2020 Feb 25]. 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


University of Southern California

20. Baurley, James William. Discovery of complex pathways from observational data.

Degree: PhD, Biostatistics, 2010, University of Southern California

 The etiology of complex diseases may involve a network of biological interactions, genetic and environmental. With the availability of high-throughput genotyping platforms, epidemiologists can thoroughly… (more)

Subjects/Keywords: pathways; complex disease; biostatistics; epidemiology; Markov Chain Monte Carlo; Bayesian model selection

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APA (6th Edition):

Baurley, J. W. (2010). Discovery of complex pathways from observational data. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/350758/rec/2024

Chicago Manual of Style (16th Edition):

Baurley, James William. “Discovery of complex pathways from observational data.” 2010. Doctoral Dissertation, University of Southern California. Accessed February 25, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/350758/rec/2024.

MLA Handbook (7th Edition):

Baurley, James William. “Discovery of complex pathways from observational data.” 2010. Web. 25 Feb 2020.

Vancouver:

Baurley JW. Discovery of complex pathways from observational data. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2020 Feb 25]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/350758/rec/2024.

Council of Science Editors:

Baurley JW. Discovery of complex pathways from observational data. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/350758/rec/2024


Washington University in St. Louis

21. Womack, Andrew. Predictive Alternatives in Bayesian Model Selection.

Degree: PhD, Mathematics, 2011, Washington University in St. Louis

Model comparison and hypothesis testing is an integral part of all data analyses. In this thesis, I present two new families of information criteria that… (more)

Subjects/Keywords: Statistics; Bayes' Factor, Bayesian model selection, Information Criteria, Posterior Predictive, Renyi Entropy

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

APA (6th Edition):

Womack, A. (2011). Predictive Alternatives in Bayesian Model Selection. (Doctoral Dissertation). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/etd/381

Chicago Manual of Style (16th Edition):

Womack, Andrew. “Predictive Alternatives in Bayesian Model Selection.” 2011. Doctoral Dissertation, Washington University in St. Louis. Accessed February 25, 2020. https://openscholarship.wustl.edu/etd/381.

MLA Handbook (7th Edition):

Womack, Andrew. “Predictive Alternatives in Bayesian Model Selection.” 2011. Web. 25 Feb 2020.

Vancouver:

Womack A. Predictive Alternatives in Bayesian Model Selection. [Internet] [Doctoral dissertation]. Washington University in St. Louis; 2011. [cited 2020 Feb 25]. Available from: https://openscholarship.wustl.edu/etd/381.

Council of Science Editors:

Womack A. Predictive Alternatives in Bayesian Model Selection. [Doctoral Dissertation]. Washington University in St. Louis; 2011. Available from: https://openscholarship.wustl.edu/etd/381


Washington University in St. Louis

22. Larsen, Trevor. Differential Estimation of Audiograms using Gaussian Process Active Model Selection.

Degree: MS, Computer Science & Engineering, 2019, Washington University in St. Louis

  Classical methods for psychometric function estimation either require excessive resources to perform, as in the method of constants, or produce only a low resolution… (more)

Subjects/Keywords: Audiogram; Machine Learning; Gaussian Process; Model Selection; Bayesian; Artificial Intelligence and Robotics; Diagnosis; Engineering

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

APA (6th Edition):

Larsen, T. (2019). Differential Estimation of Audiograms using Gaussian Process Active Model Selection. (Thesis). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/eng_etds/438

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

Chicago Manual of Style (16th Edition):

Larsen, Trevor. “Differential Estimation of Audiograms using Gaussian Process Active Model Selection.” 2019. Thesis, Washington University in St. Louis. Accessed February 25, 2020. https://openscholarship.wustl.edu/eng_etds/438.

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

MLA Handbook (7th Edition):

Larsen, Trevor. “Differential Estimation of Audiograms using Gaussian Process Active Model Selection.” 2019. Web. 25 Feb 2020.

Vancouver:

Larsen T. Differential Estimation of Audiograms using Gaussian Process Active Model Selection. [Internet] [Thesis]. Washington University in St. Louis; 2019. [cited 2020 Feb 25]. Available from: https://openscholarship.wustl.edu/eng_etds/438.

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

Council of Science Editors:

Larsen T. Differential Estimation of Audiograms using Gaussian Process Active Model Selection. [Thesis]. Washington University in St. Louis; 2019. Available from: https://openscholarship.wustl.edu/eng_etds/438

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


University of Cincinnati

23. Cornwall, Gary J. Three Essays on Bayesian Econometric Methods.

Degree: PhD, Business: Business Administration, 2017, University of Cincinnati

 This dissertation contains three essays examining new Bayesian econometric methodologies. The first develops a heterogeneous Spatial Autoregressive Model by integrating a finite mixture model structure… (more)

Subjects/Keywords: Economics; Spatial Econometrics; Predictive Model Selection; Bayesian Econometrics; Spatial Dependence; Spatial Heterogeneity

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APA (6th Edition):

Cornwall, G. J. (2017). Three Essays on Bayesian Econometric Methods. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504801632767553

Chicago Manual of Style (16th Edition):

Cornwall, Gary J. “Three Essays on Bayesian Econometric Methods.” 2017. Doctoral Dissertation, University of Cincinnati. Accessed February 25, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504801632767553.

MLA Handbook (7th Edition):

Cornwall, Gary J. “Three Essays on Bayesian Econometric Methods.” 2017. Web. 25 Feb 2020.

Vancouver:

Cornwall GJ. Three Essays on Bayesian Econometric Methods. [Internet] [Doctoral dissertation]. University of Cincinnati; 2017. [cited 2020 Feb 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504801632767553.

Council of Science Editors:

Cornwall GJ. Three Essays on Bayesian Econometric Methods. [Doctoral Dissertation]. University of Cincinnati; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504801632767553


University of Washington

24. LaMont, Colin. The search for an organizing physical framework for statistics.

Degree: PhD, 2018, University of Washington

 Theories of statistical analysis remain in conflict and contradiction. But nature reveals an elegant and coherent formulation of statistics in the thermal properties of physical… (more)

Subjects/Keywords: Bayesian; Frequentist; Information Criteria; Model Selection; Statistical Mechanics; Thermodynamics; Statistical physics; Statistics; Physics

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APA (6th Edition):

LaMont, C. (2018). The search for an organizing physical framework for statistics. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/42512

Chicago Manual of Style (16th Edition):

LaMont, Colin. “The search for an organizing physical framework for statistics.” 2018. Doctoral Dissertation, University of Washington. Accessed February 25, 2020. http://hdl.handle.net/1773/42512.

MLA Handbook (7th Edition):

LaMont, Colin. “The search for an organizing physical framework for statistics.” 2018. Web. 25 Feb 2020.

Vancouver:

LaMont C. The search for an organizing physical framework for statistics. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/1773/42512.

Council of Science Editors:

LaMont C. The search for an organizing physical framework for statistics. [Doctoral Dissertation]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/42512


University of New Mexico

25. Cheng, Yingzhe. What Affects Parents’ Choice of Milk? An Application of Bayesian Model Averaging.

Degree: Mathematics & Statistics, 2016, University of New Mexico

  This study identifies the factors that influence parents’ choice of milk for their children, using data from a unique survey administered in 2013 in… (more)

Subjects/Keywords: Model selection; Bayesian model averaging; stepwise; logistic regression; food safety; food choice; Applied Statistics; Design of Experiments and Sample Surveys

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

APA (6th Edition):

Cheng, Y. (2016). What Affects Parents’ Choice of Milk? An Application of Bayesian Model Averaging. (Masters Thesis). University of New Mexico. Retrieved from https://digitalrepository.unm.edu/math_etds/89

Chicago Manual of Style (16th Edition):

Cheng, Yingzhe. “What Affects Parents’ Choice of Milk? An Application of Bayesian Model Averaging.” 2016. Masters Thesis, University of New Mexico. Accessed February 25, 2020. https://digitalrepository.unm.edu/math_etds/89.

MLA Handbook (7th Edition):

Cheng, Yingzhe. “What Affects Parents’ Choice of Milk? An Application of Bayesian Model Averaging.” 2016. Web. 25 Feb 2020.

Vancouver:

Cheng Y. What Affects Parents’ Choice of Milk? An Application of Bayesian Model Averaging. [Internet] [Masters thesis]. University of New Mexico; 2016. [cited 2020 Feb 25]. Available from: https://digitalrepository.unm.edu/math_etds/89.

Council of Science Editors:

Cheng Y. What Affects Parents’ Choice of Milk? An Application of Bayesian Model Averaging. [Masters Thesis]. University of New Mexico; 2016. Available from: https://digitalrepository.unm.edu/math_etds/89


Universitat de Valencia

26. Montolio Breva, Laura. Model uncertainty and variable selection: an application to the modelization of FDI determinants in Europe .

Degree: 2019, Universitat de Valencia

 Las últimas décadas han visto un interés cada vez mayor en la IED, y un debate creciente sobre su modelización en términos de las variables… (more)

Subjects/Keywords: foreign direct investment determinants; gravity model; bayesian model averaging; variable selection; spanish regions; germany; generalized linear models

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APA (6th Edition):

Montolio Breva, L. (2019). Model uncertainty and variable selection: an application to the modelization of FDI determinants in Europe . (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/72103

Chicago Manual of Style (16th Edition):

Montolio Breva, Laura. “Model uncertainty and variable selection: an application to the modelization of FDI determinants in Europe .” 2019. Doctoral Dissertation, Universitat de Valencia. Accessed February 25, 2020. http://hdl.handle.net/10550/72103.

MLA Handbook (7th Edition):

Montolio Breva, Laura. “Model uncertainty and variable selection: an application to the modelization of FDI determinants in Europe .” 2019. Web. 25 Feb 2020.

Vancouver:

Montolio Breva L. Model uncertainty and variable selection: an application to the modelization of FDI determinants in Europe . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2019. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10550/72103.

Council of Science Editors:

Montolio Breva L. Model uncertainty and variable selection: an application to the modelization of FDI determinants in Europe . [Doctoral Dissertation]. Universitat de Valencia; 2019. Available from: http://hdl.handle.net/10550/72103

27. Cerezetti, Fernando Valvano. Seleção de modelos econométricos não aninhados: J-Teste e FBST.

Degree: Mestrado, Estatística, 2007, University of São Paulo

A comparação e seleção de modelos estatísticos desempenham um papel fundamental dentro da análise econométrica. No que se trata especificamente da avaliação de modelos não… (more)

Subjects/Keywords: Abordagem Bayesiana; Bayesian Theories; FBST; FBST; J-Test; J-Teste; Model Selection; Modelos Não Aninhados; Non Nested Models; Seleção de Modelos

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APA (6th Edition):

Cerezetti, F. V. (2007). Seleção de modelos econométricos não aninhados: J-Teste e FBST. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/45/45133/tde-14082013-094710/ ;

Chicago Manual of Style (16th Edition):

Cerezetti, Fernando Valvano. “Seleção de modelos econométricos não aninhados: J-Teste e FBST.” 2007. Masters Thesis, University of São Paulo. Accessed February 25, 2020. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-14082013-094710/ ;.

MLA Handbook (7th Edition):

Cerezetti, Fernando Valvano. “Seleção de modelos econométricos não aninhados: J-Teste e FBST.” 2007. Web. 25 Feb 2020.

Vancouver:

Cerezetti FV. Seleção de modelos econométricos não aninhados: J-Teste e FBST. [Internet] [Masters thesis]. University of São Paulo; 2007. [cited 2020 Feb 25]. Available from: http://www.teses.usp.br/teses/disponiveis/45/45133/tde-14082013-094710/ ;.

Council of Science Editors:

Cerezetti FV. Seleção de modelos econométricos não aninhados: J-Teste e FBST. [Masters Thesis]. University of São Paulo; 2007. Available from: http://www.teses.usp.br/teses/disponiveis/45/45133/tde-14082013-094710/ ;


Texas A&M University

28. Wu, Mingqi. Population SAMC, ChIP-chip Data Analysis and Beyond.

Degree: 2011, Texas A&M University

 This dissertation research consists of two topics, population stochastics approximation Monte Carlo (Pop-SAMC) for Baysian model selection problems and ChIP-chip data analysis. The following two… (more)

Subjects/Keywords: Markov Chain Monte Carlo; Stochastic Approximation; Metropolis-Hastings Algorithm; Bayesian Model Selection; ChIP-chip; Latent Variable; Multiple Hypothesis Test

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

APA (6th Edition):

Wu, M. (2011). Population SAMC, ChIP-chip Data Analysis and Beyond. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8752

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

Chicago Manual of Style (16th Edition):

Wu, Mingqi. “Population SAMC, ChIP-chip Data Analysis and Beyond.” 2011. Thesis, Texas A&M University. Accessed February 25, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8752.

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

MLA Handbook (7th Edition):

Wu, Mingqi. “Population SAMC, ChIP-chip Data Analysis and Beyond.” 2011. Web. 25 Feb 2020.

Vancouver:

Wu M. Population SAMC, ChIP-chip Data Analysis and Beyond. [Internet] [Thesis]. Texas A&M University; 2011. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8752.

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

Council of Science Editors:

Wu M. Population SAMC, ChIP-chip Data Analysis and Beyond. [Thesis]. Texas A&M University; 2011. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8752

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


North Carolina State University

29. Gosky, Ross Matthew. Bayesian Analysis and Matching Errors in Closed Population Capture Recapture Models.

Degree: PhD, Statistics, 2005, North Carolina State University

 Capture-Recapture models are used to estimate the unknown sizes of animal populations. When the population is closed, with constant size, during the study, eight standard… (more)

Subjects/Keywords: Bayesian; Model Selection; Matching Errors; mark-recapture; capture-recapture

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APA (6th Edition):

Gosky, R. M. (2005). Bayesian Analysis and Matching Errors in Closed Population Capture Recapture Models. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/3087

Chicago Manual of Style (16th Edition):

Gosky, Ross Matthew. “Bayesian Analysis and Matching Errors in Closed Population Capture Recapture Models.” 2005. Doctoral Dissertation, North Carolina State University. Accessed February 25, 2020. http://www.lib.ncsu.edu/resolver/1840.16/3087.

MLA Handbook (7th Edition):

Gosky, Ross Matthew. “Bayesian Analysis and Matching Errors in Closed Population Capture Recapture Models.” 2005. Web. 25 Feb 2020.

Vancouver:

Gosky RM. Bayesian Analysis and Matching Errors in Closed Population Capture Recapture Models. [Internet] [Doctoral dissertation]. North Carolina State University; 2005. [cited 2020 Feb 25]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/3087.

Council of Science Editors:

Gosky RM. Bayesian Analysis and Matching Errors in Closed Population Capture Recapture Models. [Doctoral Dissertation]. North Carolina State University; 2005. Available from: http://www.lib.ncsu.edu/resolver/1840.16/3087


Texas A&M University

30. Gao, Wenzheng. Essays on Microeconometrics with Applications to Social Network.

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

 This dissertation includes three chapters on microeconometrics with applications to social network. In the first chapter, we study identification and estimation of peer effects in… (more)

Subjects/Keywords: Microeconometrics; Social Network; Peer Effect; Homophily Effect; Bayesian Nash Equilibrium; Measurement Error; Binary Response Model; Smoothed Maximum Score Estimation; Bandwidth Selection

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APA (6th Edition):

Gao, W. (2018). Essays on Microeconometrics with Applications to Social Network. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173468

Chicago Manual of Style (16th Edition):

Gao, Wenzheng. “Essays on Microeconometrics with Applications to Social Network.” 2018. Doctoral Dissertation, Texas A&M University. Accessed February 25, 2020. http://hdl.handle.net/1969.1/173468.

MLA Handbook (7th Edition):

Gao, Wenzheng. “Essays on Microeconometrics with Applications to Social Network.” 2018. Web. 25 Feb 2020.

Vancouver:

Gao W. Essays on Microeconometrics with Applications to Social Network. [Internet] [Doctoral dissertation]. Texas A&M University; 2018. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/1969.1/173468.

Council of Science Editors:

Gao W. Essays on Microeconometrics with Applications to Social Network. [Doctoral Dissertation]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173468

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