主 题：Multi-Kink Quantile Regression for Longitudinal Data with Application to Progesterone Data Analysis
主讲人：厦门大学钟威教授
主持人：统计学院林华珍教授
时间：2022年6月29日（周三）上午10:30-11:30
直播平台及会议ID：腾讯会议，ID: 901-142-728
主办单位：统计研究中心和统计学院 科研处
主讲人简介：
钟威，现任厦门大学王亚南经济研究院、经济学院统计学与数据科学系、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，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、统计学习算法、计量经济学、统计学和数据科学的应用等研究。前后担任美国统计协会(ASA)期刊《Statistical Analysis and Data Mining》和加拿大统计学会期刊《Canadian Journal of Statistics》的AE，在The Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of Econometrics, Journal of Business & Economic Statistics, Biometrics, 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（含接收）30余篇论文，其中入选ESI前1%高被引论文2篇。主要讲授《数理统计》、《广义线性模型》、《计量经济学》、《统计数据分析》等本科和研究生课程，多次获得学院教学优秀奖，2016年获得厦门大学第五届英语教学比赛一等奖，2020年获得厦门大学第十五届青年教师技能比赛特等奖,2021年获得厦门大学教学创新大赛一等奖，2021年获得福建省“向上向善好青年”称号。
内容提要：
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. In this paper, we first propose a multi-kink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within-subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two 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 drops slightly.
