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	Robust modeling using non-elliptically contoured multivariate t distributions
	Jiang, Zhichao ; Ding, Peng
	2016
关键词	Sample selection Heavy-tailedness Heckman selection model Robit model Linear mixed-effects model Data augmentation Parameter expansion BINARY REGRESSION-MODEL DATA AUGMENTATION BAYESIAN-ANALYSIS EFFICIENT ESTIMATION PARAMETER EXPANSION ALGORITHMS MARGINALS INFERENCE FREEDOM ERROR
英文摘要	Models based on multivariate t distributions are widely applied to analyze data with heavy tails. However, all the marginal distributions of the multivariate t distributions are restricted to have the same degrees of freedom, making these models unable to describe different marginal heavy-tailedness. We generalize the traditional multivariate t distributions to non-elliptically contoured multivariate t distributions, allowing for different marginal degrees of freedom. We apply the non-elliptically contoured multivariate t distributions to three widely-used models: the Heckman selection model with different degrees of freedom for selection and outcome equations, the multivariate Robit model with different degrees of freedom for marginal responses, and the linear mixed-effects model with different degrees of freedom for random effects and within-subject errors. Based on the normal mixture representation of our t distribution, we propose efficient Bayesian inferential procedures for the model parameters based on data augmentation and parameter expansion. We show via simulation studies and real data examples that the conclusions are sensitive to the existence of different marginal heavy-tailedness. (C) 2016 Elsevier B.V. All rights reserved.; SCI(E); ARTICLE; pengdingpku@berkeley.edu; 50-63; 177
语种	英语
出处	SCI
出版者	JOURNAL OF STATISTICAL PLANNING AND INFERENCE
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/447926]
专题	数学科学学院
推荐引用方式 GB/T 7714	Jiang, Zhichao,Ding, Peng. Robust modeling using non-elliptically contoured multivariate t distributions. 2016-01-01.

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