时 间：2020年10月22日（周四）下午13:00-14:00
题 目：Multi-Kink Quantile Regression for Longitudinal Data with Application to Progesterone Data Analysis
地 点：腾讯会议ID : 691 447 102
报告人：钟威 厦门大学教授
摘 要：
Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multi-kink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. Quantile regression allows the covariate effects and the kink points to vary across different quantiles of the response and is also robust to outliers and heavy-tailed errors. In this paper, we first propose a multi-kink quantile regression for longitudinal data and develop a three-step procedure to simultaneously estimate the regression coefficients and the kink points locations. The selection consistency of the number of kink points and the asymptotic normality of the proposed estimators are established. Secondly, we construct a rank score test based on partial subgradients for the existence of kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in five to six days after ovulation and then changes to stable again or even drops slightly.
报告人简介：
钟威，现任厦门大学王亚南经济研究院和经济学院统计系教授、博士生导师。2012年获得美国宾夕法尼亚州立大学统计学博士学位，2014年和2017年分别破格晋升副教授和教授，2018年入选厦门大学“南强青年拔尖人才”（A类），国家自然科学基金优秀青年基金获得者（2019），福建省杰出青年基金获得者（2019）。主要从事高维数据统计分析和理论、统计学习和数据挖掘算法、经济计量建模、统计学和数据科学的应用等领域的研究。担任美国统计协会期刊《Statistical Analysis and Data Mining》的AE，在The Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of Business & Economic Statistics, Annals of Applied Statistics, Statistica Sinica，中国科学数学等国内外统计学权威期刊发表20多篇论文，引用次数超过800次（谷歌学术）。