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Exact Methods in Statistical Inference
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Exact Methods in Statistical Inference. |
| 개인저자 | Qiu, Yixuan. |
| 단체저자명 | Purdue University. Statistics. |
| 발행사항 | [S.l.]: Purdue University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 114 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438328679 |
| 학위논문주기 | Thesis (Ph.D.)--Purdue University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Advisers: Lingsong Zhang |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
