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020 ▼a 9780438328679
035 ▼a (MiAaPQ)AAI10830613
035 ▼a (MiAaPQ)purdue:22910
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 310
1001 ▼a Qiu, Yixuan.
24510 ▼a Exact Methods in Statistical Inference.
260 ▼a [S.l.] : ▼b Purdue University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 114 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Advisers: Lingsong Zhang
5021 ▼a Thesis (Ph.D.)--Purdue University, 2018.
520 ▼a Seeking exact methods for statistical inference problems is a fundamental and central topic in statistics. Exact methods refer to inference procedures that are able to accurately quantify the uncertainty associated with the statistical model for
520 ▼a In the first part, we revisit a classical mean comparison model for multivariate data, also known as the multivariate Behrens-Fisher problem. Specifically, we are interested in testing the mean difference between two multivariate normal samples
520 ▼a In the second part, we further extend the model to functional data, which are data viewed as functions or curves that are essentially infinite-dimensional. Functional data have become more and more prevalent with the advancement of modern data c
520 ▼a Lastly, we consider the exact inference of a class of Bayesian models in which only partial prior information is available, which is referred to as the Partial Bayes (PB) problem in this dissertation. PB problems arise when data analysts have so
590 ▼a School code: 0183.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a Purdue University. ▼b Statistics.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0183
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999463 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자