2022年1月24日上午10:00，我院特邀厦门大学钟威教授做线上学术报告，报告由学院院长王纯杰老师主持。学院部分老师，本科及研究生共165人参加本次线上学术报告。
主讲人简介：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。主要从事高维数据统计分析、非线性统计建模、计量经济学、统计学和数据科学的应用等研究。中国科学数学等国内外统计学权威期刊发表（含接收）20多篇论文，其中入选ESI前1%高被引论文2篇。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，2020年获得厦门大学“我最喜爱的十位老师”称号，2021年获得厦门大学教学创新大赛一等奖，2021年获评福建省“向上向善好青年”。
报告摘要：In this talk, I will introduce a Multi-Kink Quantile Regression (MKQR) model which assumes different linear quantile regression forms in different regions of the domain of the threshold covariate but are still continuous at kink points. I will introduce three projects on the MKQR. In the first part, we investigate parameter estimation, kink points detection and statistical inference in MKQR models for independent data. Asymptotic properties, such as selection consistency of the number of kink points and asymptotic normality of the estimators of both regression coefficients and kink effects, are established to justify the proposed method theoretically. A score test based on partial subgradients is developed to verify whether the kink effects exist or not. A new R package MultiKink is developed to easily implement the proposed methods. In the second part, we propose a multi-kink quantile regression model for longitudinal data analysis. Two estimation procedures are proposed to estimate the regression coefficients and the kink point 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. We study the application to the longitudinal progesterone data and identify two kink points in the progesterone curves over different quantiles. In the third part, we propose a multi-kink quantile regression (MKQR) model with latent homogeneous structure for panel data analysis. The proposed model accounts for both homogeneity and heterogeneity among individuals and parameters in panel data analysis. From statistical modelling point of view, it well balances the risk of misspecification and the model parsimony. From practical point of view, it is able to reveal not only the impacts of covariates in the global sense, but also individual attributes.
在报告中，钟威教授从Independent Data,Longitudinal Data以及Common Structure for Panel Data这三种类型的数据出发，详细分析多折点分位数回归问题。并且以肱三头肌皮脂厚度和女性孕酮数据为例对MKQR模型以及应用问题进行了介绍。此模型的应用不仅允许有多个折点，还可以根据数据选择折点。在非参数估计中介绍了两种解决方法：一种是核估计方法，另一种则为样条方法。讲解方式深入浅出，不仅让师生充分了解了MKQR模型，也激发了同学们对此研究方向浓厚的学习兴趣。
钟威教授的报告系统全面的对MKQR模型进行阐述，并通过实际案例的结果进行分析说明，引发了激烈的学习以及探讨，老师和学生对此次报告的知识点以及自己的疑难点向钟威教授谦虚的询问，钟威老师对算法和数据上等细节的问题耐心讲解。不仅使同学们巩固了所学知识，且对以前没有学习过的新知识有了浓浓的求知欲，开阔了学术及专业视野，为今后同学们的学习研究提供了启发！
数学与统计学院
2022年1月24日
