【外博SCI】Bayesian empirical likelihood and variable selection for censored linear model with applications to acute myelogenous leukemia data
- 所属单位：数学与统计学院
- 教研室：统计教研室
- 发表刊物：International Journal of Biomathematics
- 项目来源：省、市、自治区科技项目
- 关键字：Bayesian empirical likelihood; censored linear regression; coverage probabilities;
- 摘要：This paper develops the Bayesian empirical likelihood (BEL) method and the BEL variable
selection for linear regression models with censored data. Empirical likelihood is a
multivariate analysis tool that has been widely applied to many fields such as biomedical
and social sciences. By introducing two special priors to the empirical likelihood
function, we find two obvious superiorities of the BEL methods, that is (i) more precise
coverage probabilities of the BEL credible region and (ii) higher accuracy and correct
identification rate of the BEL model selection using an hierarchical Bayesian model, vs.
some current methods such as the LASSO, ALASSO and SCAD. The numerical simulations
and empirical analysis of two data examples show strong competitiveness of the
proposed method.
- 合写作者：赵洪梅,董小刚
- 第一作者：李纯净
- 论文类型：期刊论文
- 卷号：12
- 期号：5
- 页面范围：1
- ISSN号：5000
- 是否译文：否
- 发表时间：2019-05-29
