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Status已发表Published
TitleBayesian local influence for the growth curve model with Rao's simple covariance structure
Creator
Date Issued1996
Source PublicationJournal of Multivariate Analysis
ISSN0047-259X
Volume58Issue:1Pages:55-81
Abstract

In this paper, the Bayesian local influence approach is employed to diagnose the adequacy of the growth curve model with Rao's simple covariance structure, based on the Kullback-Leibler divergence. The Bayesian Hessian matrices of the model are investigated in detail under an abstract perturbation scheme. For illustration, covariance-weighted perturbation is considered particularly and used to analyze two real-life biological data sets, which shows that the criteria presented in this article are useful in practice. © 1996 Academic Press, Inc.

KeywordBayesian hessian matrix Bayesian local influence Covariance weighted perturbation Growth curve model Kullback-Leibler divergence Statistical diagnostics
DOI10.1006/jmva.1996.0039
URLView source
Indexed BySCIE
Language英语English
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:A1996VA19000004
Scopus ID2-s2.0-0030187399
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/2524
CollectionResearch outside affiliated institution
Affiliation
1.Hong Kong Baptist University, Hong Kong, China
2.Yunnan University, Kunming, China
3.Academia Sinica, Beijing, China
4.Academia Sinica, Beijing, China
Recommended Citation
GB/T 7714
Pan, Jianxin,Fang, Kaitai,Liski, Erkki P. Bayesian local influence for the growth curve model with Rao's simple covariance structure[J]. Journal of Multivariate Analysis, 1996, 58(1): 55-81.
APA Pan, Jianxin, Fang, Kaitai, & Liski, Erkki P. (1996). Bayesian local influence for the growth curve model with Rao's simple covariance structure. Journal of Multivariate Analysis, 58(1), 55-81.
MLA Pan, Jianxin,et al."Bayesian local influence for the growth curve model with Rao's simple covariance structure". Journal of Multivariate Analysis 58.1(1996): 55-81.
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