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University of Minnesota

1.
Johnson, Leif Thomas.
*Geometric**ergodicity* of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics.

Degree: PhD, Statistics, 2011, University of Minnesota

URL: http://purl.umn.edu/113140

► With the steady increase of affordable computing, more and more often analysts are turning to computationally intensive techniques like Markov chain Monte Carlo (MCMC). To…
(more)

Subjects/Keywords: Geometric Ergodicity; Markov chain Monte Carlo; Random-walk Metropolis; Variable Transformation; Statistics

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

APA (6^{th} Edition):

Johnson, L. T. (2011). Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/113140

Chicago Manual of Style (16^{th} Edition):

Johnson, Leif Thomas. “Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics.” 2011. Doctoral Dissertation, University of Minnesota. Accessed January 17, 2021. http://purl.umn.edu/113140.

MLA Handbook (7^{th} Edition):

Johnson, Leif Thomas. “Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics.” 2011. Web. 17 Jan 2021.

Vancouver:

Johnson LT. Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics. [Internet] [Doctoral dissertation]. University of Minnesota; 2011. [cited 2021 Jan 17]. Available from: http://purl.umn.edu/113140.

Council of Science Editors:

Johnson LT. Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics. [Doctoral Dissertation]. University of Minnesota; 2011. Available from: http://purl.umn.edu/113140

Iowa State University

2. Dixit, Anand Ulhas. Developments in MCMC diagnostics and sparse Bayesian learning models.

Degree: 2018, Iowa State University

URL: https://lib.dr.iastate.edu/etd/17175

► This dissertation consists of three research articles on the topic of Markov chain Monte Carlo (MCMC) diagnostics and sparse Bayesian learning models. The first article…
(more)

Subjects/Keywords: Geometric ergodicity; Kullback Leibler divergence; Monte Carlo standard error; Posterior impropriety; Relevance vector machine; Reproducing kernel Hilbert spaces; Statistics and Probability

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

APA (6^{th} Edition):

Dixit, A. U. (2018). Developments in MCMC diagnostics and sparse Bayesian learning models. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/17175

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Dixit, Anand Ulhas. “Developments in MCMC diagnostics and sparse Bayesian learning models.” 2018. Thesis, Iowa State University. Accessed January 17, 2021. https://lib.dr.iastate.edu/etd/17175.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Dixit, Anand Ulhas. “Developments in MCMC diagnostics and sparse Bayesian learning models.” 2018. Web. 17 Jan 2021.

Vancouver:

Dixit AU. Developments in MCMC diagnostics and sparse Bayesian learning models. [Internet] [Thesis]. Iowa State University; 2018. [cited 2021 Jan 17]. Available from: https://lib.dr.iastate.edu/etd/17175.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dixit AU. Developments in MCMC diagnostics and sparse Bayesian learning models. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/17175

Not specified: Masters Thesis or Doctoral Dissertation

University of Florida

3. Jung, Yeun Ji. Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors.

Degree: PhD, Statistics, 2015, University of Florida

URL: https://ufdc.ufl.edu/UFE0049518

Subjects/Keywords: bayesian; convergence; da; ergodicity; geometric; mcmc; regression

Record Details Similar Records

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

APA (6^{th} Edition):

Jung, Y. J. (2015). Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0049518

Chicago Manual of Style (16^{th} Edition):

Jung, Yeun Ji. “Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors.” 2015. Doctoral Dissertation, University of Florida. Accessed January 17, 2021. https://ufdc.ufl.edu/UFE0049518.

MLA Handbook (7^{th} Edition):

Jung, Yeun Ji. “Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors.” 2015. Web. 17 Jan 2021.

Vancouver:

Jung YJ. Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2021 Jan 17]. Available from: https://ufdc.ufl.edu/UFE0049518.

Council of Science Editors:

Jung YJ. Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors. [Doctoral Dissertation]. University of Florida; 2015. Available from: https://ufdc.ufl.edu/UFE0049518

The Ohio State University

4. Olsen, Andrew Nolan. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.

Degree: PhD, Statistics, 2015, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406

► Markov chains are an incredibly powerful tool for statisticians and other practitioners. They allow for random draws, though autocorrelated, to be obtained from a vast…
(more)

Subjects/Keywords: Statistics; Markov chain Monte Carlo convergence; Markov chain Monte Carlo standard errors; geometric ergodicity; scale-usage heterogeneity

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

APA (6^{th} Edition):

Olsen, A. N. (2015). When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406

Chicago Manual of Style (16^{th} Edition):

Olsen, Andrew Nolan. “When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.” 2015. Doctoral Dissertation, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406.

MLA Handbook (7^{th} Edition):

Olsen, Andrew Nolan. “When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.” 2015. Web. 17 Jan 2021.

Vancouver:

Olsen AN. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406.

Council of Science Editors:

Olsen AN. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406

5. Riou-Durand, Lionel. Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique.

Degree: Docteur es, Mathématiques appliquées, 2019, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2019SACLG006

►

La première partie de cette thèse concerne l'inférence de modèles statistiques non normalisés. Nous étudions deux méthodes d'inférence basées sur de l'échantillonnage aléatoire : Monte-Carlo… (more)

Subjects/Keywords: Échantillonnage MCMC; M-Estimateurs; Ergodicité géométrique; Temps de mélange; Couplages; Distance de Wassertein; MCMC sampling; M-Estimators; Geometric ergodicity; Mixing time; Couplings; Wasserstein distance; 510; 65C05; 62; 34A25

Record Details Similar Records

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

APA (6^{th} Edition):

Riou-Durand, L. (2019). Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2019SACLG006

Chicago Manual of Style (16^{th} Edition):

Riou-Durand, Lionel. “Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique.” 2019. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed January 17, 2021. http://www.theses.fr/2019SACLG006.

MLA Handbook (7^{th} Edition):

Riou-Durand, Lionel. “Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique.” 2019. Web. 17 Jan 2021.

Vancouver:

Riou-Durand L. Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2019. [cited 2021 Jan 17]. Available from: http://www.theses.fr/2019SACLG006.

Council of Science Editors:

Riou-Durand L. Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2019. Available from: http://www.theses.fr/2019SACLG006

University of Minnesota

6.
Johnson, Alicia A.
*Geometric**ergodicity* of Gibbs samplers.

Degree: PhD, Statistics, 2009, University of Minnesota

URL: http://purl.umn.edu/53661

► Due to a demand for reliable methods for exploring intractable probability distributions, the popularity of Markov chain Monte Carlo (MCMC) techniques continues to grow. In…
(more)

Subjects/Keywords: Convergence; Drift Conditions; Geometric Ergodicity; Gibbs Samplers; Markov chain Monte Carlo; Statistics

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Johnson, A. A. (2009). Geometric ergodicity of Gibbs samplers. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/53661

Chicago Manual of Style (16^{th} Edition):

Johnson, Alicia A. “Geometric ergodicity of Gibbs samplers.” 2009. Doctoral Dissertation, University of Minnesota. Accessed January 17, 2021. http://purl.umn.edu/53661.

MLA Handbook (7^{th} Edition):

Johnson, Alicia A. “Geometric ergodicity of Gibbs samplers.” 2009. Web. 17 Jan 2021.

Vancouver:

Johnson AA. Geometric ergodicity of Gibbs samplers. [Internet] [Doctoral dissertation]. University of Minnesota; 2009. [cited 2021 Jan 17]. Available from: http://purl.umn.edu/53661.

Council of Science Editors:

Johnson AA. Geometric ergodicity of Gibbs samplers. [Doctoral Dissertation]. University of Minnesota; 2009. Available from: http://purl.umn.edu/53661

Loughborough University

7. Zhong, Johnny. Periodic measures, transitions and exit times of stochastic differential equations.

Degree: PhD, 2019, Loughborough University

URL: https://doi.org/10.26174/thesis.lboro.11651226.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.812953

► Periodic measures are the time-periodic counterpart to invariant measures for dynamical systems that can characterise the long-term periodic behaviour of stochastic dynamical systems. In this…
(more)

Subjects/Keywords: Stochastic Differential Equations; Geometric ergodicity; partial differential equation; Feynman-Kac formula; Markov processes.; nonautonomous dynamical system; Stochastic resonance phenomena; expected exit time

Record Details Similar Records

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

APA (6^{th} Edition):

Zhong, J. (2019). Periodic measures, transitions and exit times of stochastic differential equations. (Doctoral Dissertation). Loughborough University. Retrieved from https://doi.org/10.26174/thesis.lboro.11651226.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.812953

Chicago Manual of Style (16^{th} Edition):

Zhong, Johnny. “Periodic measures, transitions and exit times of stochastic differential equations.” 2019. Doctoral Dissertation, Loughborough University. Accessed January 17, 2021. https://doi.org/10.26174/thesis.lboro.11651226.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.812953.

MLA Handbook (7^{th} Edition):

Zhong, Johnny. “Periodic measures, transitions and exit times of stochastic differential equations.” 2019. Web. 17 Jan 2021.

Vancouver:

Zhong J. Periodic measures, transitions and exit times of stochastic differential equations. [Internet] [Doctoral dissertation]. Loughborough University; 2019. [cited 2021 Jan 17]. Available from: https://doi.org/10.26174/thesis.lboro.11651226.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.812953.

Council of Science Editors:

Zhong J. Periodic measures, transitions and exit times of stochastic differential equations. [Doctoral Dissertation]. Loughborough University; 2019. Available from: https://doi.org/10.26174/thesis.lboro.11651226.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.812953

University of Maryland

8. Li, Ziliang. Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model.

Degree: Mathematical Statistics, 2010, University of Maryland

URL: http://hdl.handle.net/1903/10964

► In the study of finance, likelihood based or moment based methods are frequently used to estimate parameters for various kinds of models given the sampled…
(more)

Subjects/Keywords: Statistics; Economics, Finance; Geometric Ergodicity; Kernel Density Estimate; Method of Moments; Minimum Disparity Estimator; Stochastic Volatility model

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Li, Z. (2010). Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/10964

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Li, Ziliang. “Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model.” 2010. Thesis, University of Maryland. Accessed January 17, 2021. http://hdl.handle.net/1903/10964.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Li, Ziliang. “Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model.” 2010. Web. 17 Jan 2021.

Vancouver:

Li Z. Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model. [Internet] [Thesis]. University of Maryland; 2010. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/1903/10964.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li Z. Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model. [Thesis]. University of Maryland; 2010. Available from: http://hdl.handle.net/1903/10964

Not specified: Masters Thesis or Doctoral Dissertation

University of Florida

9. Tan, Aixin. Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models.

Degree: PhD, Statistics, 2009, University of Florida

URL: https://ufdc.ufl.edu/UFE0024910

► Markov chain Monte Carlo (MCMC) methods have received considerable attention as powerful computing tools in Bayesian statistical analysis. The idea is to produce Markov chain…
(more)

Subjects/Keywords: Consistent estimators; Ergodic theory; Estimators; Markov chains; Perceptron convergence procedure; Simulations; Standard error; Statistics; Sufficient conditions; Tours; asymptotic, convergence, drift, ergodicity, geometric, minorization, variance

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Tan, A. (2009). Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0024910

Chicago Manual of Style (16^{th} Edition):

Tan, Aixin. “Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models.” 2009. Doctoral Dissertation, University of Florida. Accessed January 17, 2021. https://ufdc.ufl.edu/UFE0024910.

MLA Handbook (7^{th} Edition):

Tan, Aixin. “Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models.” 2009. Web. 17 Jan 2021.

Vancouver:

Tan A. Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2021 Jan 17]. Available from: https://ufdc.ufl.edu/UFE0024910.

Council of Science Editors:

Tan A. Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models. [Doctoral Dissertation]. University of Florida; 2009. Available from: https://ufdc.ufl.edu/UFE0024910

Virginia Tech

10. Sun, Peng. Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models.

Degree: PhD, Statistics, 2016, Virginia Tech

URL: http://hdl.handle.net/10919/78189

Subjects/Keywords: Additive Model; Bayes factor; Cubic Splines; Dual-Semiparametric Regression; Generalized Polya urn; Geometric ergodicity; Gibbs sampling; Metropolis-Hastings; Nonparametric Bayesian Model; Ordinal data; Parameterization; Semiparametric Regr

Record Details Similar Records

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

APA (6^{th} Edition):

Sun, P. (2016). Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78189

Chicago Manual of Style (16^{th} Edition):

Sun, Peng. “Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 17, 2021. http://hdl.handle.net/10919/78189.

MLA Handbook (7^{th} Edition):

Sun, Peng. “Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models.” 2016. Web. 17 Jan 2021.

Vancouver:

Sun P. Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/10919/78189.

Council of Science Editors:

Sun P. Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/78189