主 题：Goodness-of-Fit Test for Model Specification
主讲人：西门菲沙大学 Qian (Michelle) Zhou 博士
主持人：张术林博士
时 间：2014年3月6日下午13：00
地 点：通博楼408教室
主办单位：统计学院 统计研究中心 科研处
主讲人简介：
Michelle Zhou received her M.Sc. from the University of Waterloo, Canada under the supervision of Dr. Mu Zhu, and a Ph.D. from the University of Waterloo in 2009 under the supervision of Dr. Mary Thompson and Dr. Peter Song. Afterwards, she was a Postdoctoral Fellow at Harvard School of Public Health working with Dr. Tianxi Cai and Dr. Xihong Lin until June 2012. Currently, Dr. Zhou is an assistant professor in the Department of Statistics and Actuarial Science at Simon Fraser University, Canada. Her research focuses on model diagnosis, model selection, survival analysis, longitudinal data analysis, risk prediction, biomarker evaluation, and personalized medicine.
内容提要：
In this talk, I will introduce information ratio (IR) statistic to test for model misspecification in various models. The IR test was first proposed in my Ph.D. thesis to test for model misspecification of variance/covariance structure in quasi-likelihood inference for cross-sectional data or longitudinal data. The statistic is constructed via a contrast between two forms of information matrix: the negative sensitivity matrix and variability matrix. Under the null hypothesis that the variance/covariance structure is correctly specified, we show that the proposed test statistic is asymptotically distributed as a normal random variable with mean equal to the dimension of the parameter space. Later, this test was further developed to test for model misspecification on parametric structures in stochastic diffuse models. Afterwards, we extend our method to test for model misspecification in parametric copula functions of semi-parametric copula models. We propose a new test constructed via the contrast between "in-sample" and "out-of-sample" pseudo-likelihoods.
