Semiparametric Bayesian inference for mean-covariance regression models |
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Authors: | Han Jun Yu Jun Shan Shen Zhao Nan Li Xiang Zhong Fang |
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Institution: | 1. School of Mathematical Sciences, Peking University, Beijing 100871, P. R. China;2. School of Statistics, Capital University of Economics and Business, Beijing 100070, P. R. China |
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Abstract: | In this paper, we propose a Bayesian semiparametric mean-covariance regression model with known covariance structures. A mixture model is used to describe the potential non-normal distribution of the regression errors. Moreover, an empirical likelihood adjusted mixture of Dirichlet process model is constructed to produce distributions with given mean and variance constraints. We illustrate through simulation studies that the proposed method provides better estimations in some non-normal cases. We also demonstrate the implementation of our method by analyzing the data set from a sleep deprivation study. |
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Keywords: | Clustered data Dirichlet process empirical likelihood moment constraints nonparametric Bayes |
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