文章基本信息

标题：Influence Diagnostics for Skew-Normal Linear Mixed Models
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作者：Heleno Bolfarine ; Universidade de S\~ao Paulo ; S\~ao Paulo 等
期刊名称：Sankhya. Series A, mathematical statistics and probability
印刷版ISSN：0976-836X
电子版ISSN：0976-8378
出版年度：2007
卷号：69
期号：04
出版社：Indian Statistical Institute
摘要：Normality (symmetry) of the random effects is a routine assumption in linear mixed models but it may, sometimes, be unrealistic, obscuring important features of among-subjects variation. We relax this assumption by assuming that the random effects density is skew-normal, considered as an extension of the univariate version proposed by Sahu, Dey and Branco ({\it CJS}, 2003). Following Zhu and Lee ({\it JRSSB}, 2001), we implement an EM-type algorithm to parameter estimation and then using the related conditional expectation of the complete-data log-likelihood function, develop diagnostic measures for implementing the local influence approach under four model perturbation schemes. Results obtained from simulated and real data sets are reported illustrating the usefulness of the approach.
关键词：Skew-normal distribution, EM-algorithm, skewness, local influence, case deletion.