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Advanced statistical computing: Hamiltonian assisted Markov chain Monte Carlo and imputation maximization stochastic approximation
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Song, Zexi. Advanced statistical computing: Hamiltonian assisted Markov chain Monte Carlo and imputation maximization stochastic approximation. Retrieved from https://doi.org/doi:10.7282/t3-4zea-3d22

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TitleAdvanced statistical computing: Hamiltonian assisted Markov chain Monte Carlo and imputation maximization stochastic approximation
NameSong, Zexi (author); Tan, Zhiqiang (chair); Chen, Rong (member); Wang, Guanyang (member); Lu, Jianfeng (member); Rutgers University; School of Graduate Studies
Date Created2022
Other Date2022-05 (degree)
SubjectStatistics, Langevin dynamics, Latent variable models, Markov chain Monte Carlo, Metropolis-Hastings, Stochastic approximation
Extent225 pages : illustrations
DescriptionIn this dissertation we develop novel methods in two areas of advanced statistical computing. The first area is Markov chain Monte Carlo (MCMC) and the second one is stochastic approximation (SA).
For the MCMC part, we develop and investigate in depth over two chapters, a broad class of irreversible sampling algorithms, called Hamiltonian assisted Metropolis sampling (HAMS). Furthermore, we formulate a framework of generalized Metropolis-Hastings sampling, which not only highlights our construction of HAMS at an abstract level, but also facilitates possible further development of irreversible MCMC algorithms. Extensive numerical experiments are conducted where the proposed HAMS algorithms consistently outperform existing methods.
For the SA part, we propose a new method called imputation maximization stochastic approximation (IMSA) which is suitable for estimation in latent variable models. We focus on its application in generalized linear mixed models and demonstrate its advantages over existing likelihood based methods through simulation studies.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-4zea-3d22
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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