报告题目:Fast QLB algorithm and hypothesis tests in logistic model for ophthalmologic bilateral correlated data

报告摘要:In ophthalmologic or otolaryngologic studies, bilateral correlated data often arise when observations involving paired organs (e.g., eyes, ears) are measured from each subject. Based on the model of Donner (1989), in this paper, we focus on investigating the relationship between the disease probability and covariates (such as ages, weights, gender and so on) via the logistic regression for the analysis of bilateral correlated data. We first propose a new minorization-maximization} (MM) algorithm and a fast quadratic lower bound (QLB) algorithm to calculate the maximum likelihood estimates of the vector of regression coefficients, and then develop three large-sample tests (i.e., the likelihood ratio test, Wald test and score test) to test if covariates have a significant impact on the disease probability. Simulation studies are conducted to evaluate the performance of the proposed fast QLB algorithm and three testing methods. A real ophthalmologic data set in Iran is used to illustrate the proposed methods.

报告人介绍: 田国梁，现任南方科技大学数学系教授、博士生导师。田教授于1988年获得武汉大学统计学硕士学位、于1998年获得中国科学院应用数学研究所统计学博士学位。从1998至2002年, 他分别在北京大学概率统计系和美国田纳西州孟斐斯市的 St. Jude 儿童研究医院生物统计系从事博士后研究, 2002年至2008年他在美国马里兰大学Greenbaum 癌症中心任 Senior Bio-statistician。2008年至2016年他在香港大学统计及精算学系任副教授、博士生导师。田教授是国际统计学会 (ISI) 当选会员, 他担任 Computational Statistics & Data Analysis, Statistics and Its Interface 等四个国际统计学杂志的副主编。

报告时间：2018年12月20日（周四）下午3:30

报告地点：长清湖校区A232报告厅

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