报告人：王兆军
报告地点：数学与统计学院415室
报告时间：2018年07月02日星期一16:00-17:00
邀请人：
报告摘要：
Lack-of-fit checking for parametric models is essential in reducing misspecification. However, for massive datasets which are increasingly prevalent, classical tests become prohibitively costly in computation and its feasibility is questionable even with modern parallel computing platforms. Building on the divide and conquer strategy, we propose a new nonparametric testing method, that is fast to compute and easy to implement with only one tuning parameter determined by a given time budget. Under mild conditions, we show that the proposed test statistic is asymptotically equivalent to that based on the whole data. Benefiting from using the sample-splitting idea for choosing the smoothing parameter, the proposed test is able to retain the type-I error rate pretty well with asymptotic distributions and achieves adaptive rate-optimal detection properties. Its advantage relative to existing methods is also demonstrated in numerical simulations and a data illustration.
主讲人简介：
王兆军为南开大学统计与数据科学学院教授、副院长，教育部长江学者特聘教授，国务院学位委员会第七届学科评议组成员(统计学)、中国现场统计研究会副理事长、中国现场统计研究会生存分析分会副理事长、中国工业统计学教学研究会副理事长、中国统计教育学会高等教育分会副会长、天津市现场统计研究会理事长、天津市统计学会副会长。曾获全国百篇优博指导教师、教育部全国高校自然科学二等奖及天津市自然科学一等奖。目前为《数理统计与管理》副主编，《数学进展》和《统计信息论坛》编委。目前主持国家自然科学基金重点项目一项，并已完成多项国家面上项目。已在Journal of the American Statistical Association、 Annals of Statistics、Biometrika、Statistica Sinica、Journal of Quality Technology 、Journal of Multivariate Analysis、Technometrics、Test等专业顶级期刊上发表数十篇专业学术论文。
