Influence Diagnostics for Skew-Normal Linear Mixed Models
Influence Diagnostics for Skew-Normal Linear Mixed Models
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发表时间：
2016
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通讯作者：
H. Bolfarine;L. C. Montenegro;V. H. Lachos
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作者：
H. Bolfarine;L. C. Montenegro;V. H. Lachos
随机效应的正态(对称性)是线性混合模型中的常规假设，但它有时可能是不现实的，掩盖了受试者之间变化的重要特征。我们放宽了这一假设，假设随机效应密度是倾斜正态的，被认为是Sahu，Dey和Branco(CJS，2003)提出的单变量版本的推广。在朱和李(JRSSB，2001)的基础上，我们实现了EM型的参数估计算法，然后利用完全数据对数似然函数的相关条件期望，在四种模型摄动方案下给出了实现局部影响方法的诊断措施。报告了从模拟和真实数据集获得的结果，说明了该方法的有效性。AMS(2000)学科分类。初级62H12，60E05。
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 (CJS, 2003). Following Zhu and Lee (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. AMS (2000) subject classification. Primary 62H12, 60E05.