Statistics and Its Interface

Volume 13 (2020)

Number 1

Bayesian Variable Selection and Estimation in Joint Confirmatory Factor Analysis--Cox Model

Pages: 49 – 63

DOI: https://dx.doi.org/10.4310/SII.2020.v13.n1.a5

Authors

Chenyi Liang (Department of Statistics, Sun Yat-sen University, Guangzhou, China)

Jingheng Cai (Department of Statistics, Sun Yat-sen University, Guangzhou, China)

Abstract

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.

Keywords

Confirmatory factor analysis model, Cox model, Latent variables, Bayesian adaptive lasso, Variable selection

2010 Mathematics Subject Classification

62F15, 62N01

Full Text (PDF format)

Received 9 July 2018

Accepted 2 August 2019

Published 7 November 2019