Bayesian variable selection and estimation in joint confirmatory factor analysis-Cox model

被引：1
|
作者：
Liang, Chenyi [1 ]
Cai, Jingheng [1 ]
机构：
[1] Sun Yat Sen Univ, Dept Stat, Guangzhou, Guangdong, Peoples R China
来源：
STATISTICS AND ITS INTERFACE | 2020年 / 13卷 / 01期
关键词：
Bayesian adaptive Lasso; Proportional hazards model; Latent variables; Bladder cancer; ADAPTIVE LASSO; REGRESSION; DISTRIBUTIONS; SHRINKAGE;
D O I：
10.4310/SII.2020.v13.n1.a5
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In this article, we propose the joint confirmatory factor analysis-Cox model to assess the effects of observed and latent risk factors on survival time. The Bayesian adaptive Lasso procedure is developed to simultaneously conduct estimation and variable selection for the proposed model. Nice features including the empirical performance of the proposed method are demonstrated by simulation studies. The proposed method is applied to analyze the bladder cancer data set obtained from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute.
引用
收藏
页码：49 / 63
页数：15
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