LDR | | 00000nmm u2200205 4500 |
001 | | 000000330640 |
005 | | 20241104112720 |
008 | | 181129s2015 ||| | | | eng d |
020 | |
▼a 9780438097315 |
035 | |
▼a (MiAaPQ)AAI10891768 |
035 | |
▼a (MiAaPQ)OhioLINK:osu1449069220 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
049 | 1 |
▼f DP |
082 | 0 |
▼a 310 |
100 | 1 |
▼a Wang, Xiaomu. |
245 | 10 |
▼a Robust Bayes in Hierarchical Modeling and Empirical Bayes Analysis in Multivariate Estimation. |
260 | |
▼a [S.l.] :
▼b The Ohio State University.,
▼c 2015 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2015 |
300 | |
▼a 99 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
500 | |
▼a Adviser: Berliner L. Mark. |
502 | 1 |
▼a Thesis (Ph.D.)--The Ohio State University, 2015. |
520 | |
▼a With the modern development of statistical data analysis, the data volume increases and the data dimension increases correspondingly. This thesis investigates two classic Bayes problems: robust Bayes analysis in hierarchical modeling and empiric |
520 | |
▼a In Bayesian analysis, it is difficult to develop a single prior to completely and fully quantify our prior information. Thereby, o-contamination classes have become popular models of the uncertainty in prior distributions. For the first part of |
520 | |
▼a When a class of priors is assumed in Bayesian analysis, it is vital to consider a decision rule corresponding to this class. In a multivariate estimation setting, for the second part of this thesis, I focus on research to find a compromise betwe |
590 | |
▼a School code: 0168. |
650 | 4 |
▼a Statistics. |
690 | |
▼a 0463 |
710 | 20 |
▼a The Ohio State University.
▼b Statistics. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0168 |
791 | |
▼a Ph.D. |
792 | |
▼a 2015 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000267
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자
▼b 관리자 |