論文

査読有り 筆頭著者 最終著者 責任著者
2008年3月

Bayesian significance testing and multiple comparisons from MCMC outputs

COMPUTATIONAL STATISTICS & DATA ANALYSIS
  • Takahiro Hoshino

52
7
開始ページ
3543
終了ページ
3559
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.csda.2007.11.009
出版者・発行元
ELSEVIER SCIENCE BV

This article proposes a Bayesian method to directly evaluate and test hypotheses in multiple comparisons. Transformation and integration over the coordinates relevant to the hypothesis are shown to enable us to directly test the hypotheses expressed as a linear equation of a parameter vector, given a linear constraint. When the conditional posterior distribution of the parameter vector we are interested in is the multivariate normal distribution, the proposed method can be applied to calculate the p-value of hypotheses pertaining to the parameters in any complex model such as generalized linear mixed effect models with latent variables, by using outputs from Markov chain Monte Carlo (MCMC) methods. Further, the proposed testing can be implemented without prior information. Some applications are presented, and the simulation results are provided to compare the powers of this method with those of other methods of conventional multiple comparisons. Simulation studies have shown that the proposed method is valid for multiple comparisons under nonequivalent variances and mean comparisons in latent variable modeling with categorical variables. (c) 2007 Elsevier B.V. All rights reserved.

Web of Science ® 被引用回数 : 2

リンク情報
DOI
https://doi.org/10.1016/j.csda.2007.11.009
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000255145900019&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.csda.2007.11.009
  • ISSN : 0167-9473
  • Web of Science ID : WOS:000255145900019

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