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Asymptotic Posterior Approximation and Efficient MCMC Sampling for Generalized Linear Mixed Models
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Asymptotic Posterior Approximation and Efficient MCMC Sampling for Generalized Linear Mixed Models. |
| 개인저자 | Berman, Brandon. |
| 단체저자명 | University of California, Irvine. Statistics - Ph.D.. |
| 발행사항 | [S.l.]: University of California, Irvine., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 215 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085611336 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Irvine, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Johnson, Wesley |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Generalized linear mixed models (GLMMs) provide statisticians, scientists, and analysts great flexibility to model data in a variety of situations. However, GLMMs frequently produce unrecognizable conditional distributions when attempting to analyze them in the Bayesian framework with Gibbs sampling. Traditionally, complex sampling schemes are used to obtain samples from these distributions. Our focus is to obtain asymptotic normal for these distributions and apply the theoretical results to speed up the process of MCMC. |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
