报告题目：General matching quantiles M-estimation
报告摘要：In this talk, we first describe the background/motivation of matching quantiles estimation (MQE). After it, we briefly introduce M-estimation which is a robust alternative to the ordinary least-squares. Then we present a matching quantiles M-estimation (MQME) method. Since the number of variables may be large in many real problems, a `sparse' representation is highly desirable. We thus integrate the proposed MQME with adaptive Lasso for selecting informative variables. In order to compute MQME, we develop an iterative algorithm. Moreover, we show that the (sparse) matching quantiles M-estimator possesses the property of asymptotic consistency. Our simulations demonstrate the efficient finite-sample performance of the proposed method. We end this talk by presenting an illustrative real case study.
报告时间：2023年10月25日，星期三，下午15:30---16:30
报告地点：williamhill官网-中文官网犀浦校区，3号教学楼30429会议室
报告人简介：吴月华，加拿大约克大学数学与统计系教授。1989年获得美国匹兹堡大学统计学博士学位，师从世界著名统计学家C. R. Rao。研究领域非常广泛，包括空间统计、M-估计、模型选择、变点检测、非参数估计、金融统计等，以及在环境科学、信息科学、计量经济学、生物医学等领域中的应用，目前是国际统计学会的当选会员。在Proceeding of National Academy Science, USA (美国国家科学院院刊), Biometrika, Statistica Sinica, Computational Statistics & Data Analysis，Journal of Multivariate Analysis等期刊发表学术论文100多篇。承担加拿大国家自然科学基金、加拿大环境署等多项科研项目。
