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008181129s2015 ||| | | | eng d
020 ▼a 9780438097315
035 ▼a (MiAaPQ)AAI10891768
035 ▼a (MiAaPQ)OhioLINK:osu1449069220
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 310
1001 ▼a Wang, Xiaomu.
24510 ▼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.
5021 ▼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
71020 ▼a The Ohio State University. ▼b Statistics.
7730 ▼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
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000267 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자