Bayesian quantile regression for partially linear mixed-effects models in skew longitudinal data
Publish place: ninth iranian conference on on bioinformatics
Publish Year: 1398
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
IBIS09_060
تاریخ نمایه سازی: 19 اسفند 1399
Abstract:
The linear mixed model has become the most frequently used analytic tool for longitu- dinal data analysis with continuous repeated measures. A linear mixed model consistsof fixed effects and random effects and is characterized by the ability of accounting for both the between- and within- subject variabilities. Following Lin and Lee (2008), in this article we advocate the use of multivariate skew-elliptical distribution, so that the Bayesian quantile regression for skew-elliptical partially linear mixed model is developed. Using the connection between asymmet- ric Laplace distribution (ALD) and quantile regression discussed by Yu and Moyeed (2001), we develop a fully Bayesian hierarchical model to estimate the parameters of conditional quantile functions with random effects by adopting an ALD for random errors and a multivariate skew-elliptical distribution introduced by Sahu et al. (2003), for random effects.
Authors
Zeinab Morshedi
Department of Statistics, University of Zanjan, Zanjan, Iran
Ali Aghamohammadi
Department of Statistics, University of Zanjan, Zanjan, Iran