报告人：蔡敬衡(中山大学)

报告题目：Bayesian Analysis of Transformation Latent Variable Models with Multivariate Censored Data

报告摘要：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.

报告时间：2018年5月31日（星期四）下午15：00--16：00

报告地点：科技楼南楼602