| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000432263 |
| 005 | | 20200224115847 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085611336 |
| 035 | |
▼a (MiAaPQ)AAI13897844 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Berman, Brandon. |
| 245 | 10 |
▼a Asymptotic Posterior Approximation and Efficient MCMC Sampling for Generalized Linear Mixed Models. |
| 260 | |
▼a [S.l.]:
▼b University of California, Irvine.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 215 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-02, Section: B. |
| 500 | |
▼a Advisor: Johnson, Wesley |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Irvine, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a 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. |
| 590 | |
▼a School code: 0030. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a University of California, Irvine.
▼b Statistics - Ph.D.. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-02B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0030 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491886
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |