报告题目：Estimation and inference for multi-kink expectile regression with longitudinal data

报 告 人：王磊 副教授 南开大学

报告时间：2021年11月22日 9:00-10:00

报告地点：腾讯会议 ID：514 451 479会议密码：654321

校内联系人：朱复康 fzhu@jlu.edu.cn

报告摘要：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，博士生导师。研究方向是复杂数据分析和统计学习，已在Biometrika、Bernoulli、Statistica Sinica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面上项目、青年项目及天津市自然科学基金各一项。