报 告 人：宋心远 教授（香港中文大学 ）
报告题目：Additive hazards model with latent variables
报告时间：2016年5月20日（周五）8:30
报告地点：静远楼1506学术报告厅
报告摘要：We propose an additive hazards model with latent variables to investigate the observed and latent risk factors of the failure time of interest. Each latent risk factor is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a hybrid procedure that combines the expectation–maximization (EM) algorithm and the borrow-strength estimation approach to estimate the model parameters. We establish the consistency and asymptotic normality of the parameter estimators. Various nice features, including finite sample performance of the proposed methodology, are demonstrated by simulation studies. Our model is applied to a study concerning the risk factors of chronic kidney disease for Type 2 diabetic patients.
报告人简介: Xinyuan Song is a professor in the Department of Statistics, an Assistant Dean in the Faculty of Science, and the Graduate Division Head in the Department of Statistics, The Chinese University of Hong Kong. Her research interests are latent variable models, Bayesian methods, statistical computing, and survival analysis. She serves as an associate editor or editorial board member for several leading journals, including Psychometrika, Biometrics, Structural Equation Modeling: A Multidisciplinary Journal, and Computational Statistics & Data Analysis. She has published over 100 papers in prestigious international journals. Her recent publications appeared in Journal of the American Statistical Association, Biometrika, Biometrics, Psychometrika, Structural Equation Modeling: A Multidisciplinary Journal, and Sociological Methods and Research.