【主 题】A fast simultaneous multiple change-points detection in generalized linear models

【报告人】Yuehua Amy Wu ,教授York University

【时 间】2019年5月6日（星期一）10:00-11:00

【地 点】上海财经大学统计与管理学院大楼1208会议室【

摘 要】In this talk, we focus on the problem of multiple change points estimation in GLMs in which both number of change points and their locations are unknown. We propose a simultaneous multiple change points estimation method which first partitions the data sequence into several segments to construct a new design matrix, secondly convert the multiple change points estimation problem into a variable selection problem, and then estimate the regression coefficients by maximizing a penalized likelihood function. The consistency of the coefficient estimator is established in which the number of coefficients can diverge as the sample size goes to infinity. The nonzero coefficient estimates provide the information about which segments potentially contain a change point. An algorithm is provided to estimate the multiple change points. Simulation studies are conducted for both logistic and log-linear models. A real data application is also presented.【

嘉宾简介】Dr. Yuehua Amy Wu is the Professor of Statistics, York University, Canada. Her research interests include M-estimation, Multiple change-point analysis, and High-dimensional statistics. et al.