title: Bayesian analysis of transformation latent variable models with multivariate censored data.
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.
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
authors.forename: Xin-Yuan
authors.initials: XY
authors.lastname: Song
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
authors.forename: Deng
authors.initials: D
authors.lastname: Pan
authors.affiliation: School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou, China.
authors.forename: Peng-Fei
authors.initials: PF
authors.lastname: Liu
authors.affiliation: Department of Statistics, Sun Yat-sen University, Guangzhou, China caijheng@mail.sysu.edu.cn.
authors.forename: Jing-Heng
authors.initials: JH
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002318:Cardiovascular Diseases; D003924:Diabetes Mellitus, Type 2; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis
keywords: Bayesian P-splines; Markov chain Monte Carlo methods; latent variables; semiparametric model; transformation model