Bayesian empirical likelihood and variable selbeection for censored linear model with applications to acute myelogenous leukemia data
- DOI码：10.1142/S1793524519500505
- 发表刊物：International Journal of Biomathematics
- 关键字：Bayesian empirical likelihood; censored linear regression; coverage probabilities; spike-and-slab prior
- 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：赵红梅
- 第一作者：李纯净
- 论文类型：期刊论文
- 通讯作者：董小刚
- 学科门类：理学
- 文献类型：J
- 卷号：12
- 期号：5
- 页面范围：1—19
- ISSN号：1793-5245
- 是否译文：否
- 发表时间：2019-05-29
- 收录刊物：SCI
- 发布期刊链接：https://www.worldscientific.com/doi/10.1142/S1793524519500505
