时 间：2021年11月18日（周四）13:50-14:40
地 点：线上，腾讯会议305 493 290
题 目：Estimation and inference for multi-kink expectile regression with longitudinal data
主讲人：王磊 南开大学副研究员
主持人：唐炎林 研究员
摘 要：
In this paper, we investigate parameter estimation, kink points testing and statistical inference for a longitudinal multi-kink expectile regression model. The estimators for the kink locations and regression coefficients are obtained by using a bootstrap restarting iterative algorithm to avoid local minima. A backward selection procedure based on a modified BIC is applied to estimate the number of kink points. We theoretically demonstrate the number selection consistency of kink points and the asymptotic normality of all estimators. In particular, the estimators of kink locations are shown to achieve root-n consistency. A weighted cumulative sum type statistic is proposed to test the existence of kink effects at a given expectile, and its limiting distributions are derived under both the null and the local alternative hypotheses. The traditional Wald-type and cluster bootstrap confidence intervals for kink locations are also constructed. Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic errors. Two applications to the Nation Growth, Lung and Health Study and Capital Bike sharing dataset in Washington D.C. are also presented..
报告人简介：
王磊，南开大学统计与数据科学学院副研究员，博导，南开大学百名青年学科带头人，天津市131创新型人才第三层次。研究方向是统计学习和复杂数据分析，已在Biometrika、Bernoulli、Statistica Sinica、Scandinavian Journal of Statistics等统计学杂志发表学术论文30多篇，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。现任中国现场统计研究会生存分析分会副秘书长，Journal of Nonparametics Statistics的Associate Editor，泛华统计协会永久会员, 荣获上海市优秀博士学位论文等。