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学术报告:Recent Advances in Modeling Complex Data with Latent Variable

发布时间:2018年11月21日 浏览次数:发布者:mathky

报告人:宋心远教授

         香港中文大学统计系

报告题目:Recent Advances in Modeling Complex Data with Latent Variable

        (暨香港中文大学统计系研究生招生宣讲会)

报告时间:2018年11月24日下午16:00

报告地点:实验楼105

摘要:This talk introduces joint modeling approaches for analyzing complex data with latent variables. Several statistical models, including scalar-on-imaging regression model, two-part model, and additive mean residual life model, are considered to analyze imaging data, zero inflated or semi-continuous data, and time-to-event data in the presence of latent variables. The Bayesian approach and estimating equation method are used to conduct statistical inference. Several real applications to medical and financial studies are presented.

报告人简介: 宋心远,香港中文大学统计系教授,系主任。博士毕业于香港中文大学。主要研究方向为潜变量模型,贝叶斯方法,统计计算,半参非参模型,生存分析等,曾担任Structural Equation Modeling, Biometrics, Computational Statistics & Data Analysis等国际顶级期刊的副主编。并已在Journal of the American Statistical Association、Biometrika、Biometrics、Bioinformatics等国际期刊上发表了超过100篇论文。

联系人:胡杰助理教授

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