时间：2019年12月6日(周五) 10:00-11:00
地点：中北校区理科大楼A1716报告厅
题目：Improved empirical likelihood inference and variable selection for GLM with longitudinal nonignorable dropouts
报告人：王磊 助理教授 南开大学 统计与数据科学学院
摘要：In this paper, we propose improved statistical inference and variable selection methods for generalized linear models (GLM) based on empirical likelihood approach that accommodates both the within-subject correlations and nonignorable dropouts. We first apply the generalized method of moments (GMM) to estimate the parameters in the nonignorable dropout propensity based on an instrument. The inverse probability weighting is applied to obtain the bias-corrected generalized estimating equations (GEEs) and then we borrow the idea of quadratic inference function (QIF) and hybrid GEE to construct the empirical likelihood procedures for longitudinal data with non-ignorable dropouts, respectively. Two different classes of estimators and their confidence regions are derived. Further, the penalized EL method and algorithm for variable selection are investigated. The finite-sample performance of the proposed estimators is studied through simulation, and an application to HIV-CD4 data set is also presented.
报告人简介：王磊，南开大学数据与统计学院助理教授，南开大学百名青年学科带头人，主持国家自然科学基金青年、面上项目及天津市自然科学基金各一项，研究兴趣为复杂数据统计分析， 研究成果发表在Biometrika、Statistica Sinica、Scandinavian Journal of Statistics、Computational Statistics and Data Analysis等统计学杂志。