A linear mixed model for analyzing longitudinal skew-normal responses with random dropout

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• 作者：M. Ganjali<sup> ; sup><sup>m-ganjali@sbu.ac.irsup><sup> ; sup><sup>m_ganjali43@yahoo.comsup> ; T. Baghfalaki <sup>t.baghfalaki@yahoo.comsup> ; M. Khazaei <sup>m-khazaei@sbu.ac.irsup>
• 关键词：62P10
• 刊名：Journal of the Korean Statistical Society
• 出版年：2013
• 出版时间：June, 2013
• 年：2013
• 卷：42
• 期：2
• 页码：149-160
• 全文大小：618 K

文摘

In this paper, a linear mixed effects model is used to fit skewed longitudinal data in the presence of dropout. Two distributional assumptions are considered to produce background for heavy tailed models. One is the linear mixed model with skew-normal random effects and normal errors and the other one is the linear mixed model with skew-normal errors and normal random effects. An ECM algorithm is developed to obtain the parameter estimates. Also an empirical Bayes approach is used for estimating random effects. A simulation study is implemented to investigate the performance of the presented algorithm. Results of an application are also reported where standard errors of estimates are calculated using the Bootstrap approach.