Bayesian Analysis of Transformation Latent Variable Models with Multivariate Censored Data

• Activity Number: 589
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Xinyuan Song* and Deng Pan and Pengfei Liu and Jingheng Cai
• Companies: and Huazhong University of Science and Techonology and Jiangsu Normal University and Sun Yat-Sen University
• Keywords: transformation model ; latent variables ; semiparametric model ; Bayesian P-splines ; MCMC methods

Abstract

Transformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were assessed based on several correlated observed indicators through measurement equations. A Bayesian approach was developed with Bayesian P-splines technique and the Markov chain Monte Carlo algorithm to estimate the unknown parameters and transformation functions. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method was applied to analyze a cardiovascular disease data set.