报告人：王兆军教授 南开大学
报告题目：A scalable nonparametric specification testing in massive data
时 间：2016年12月13日 (星期二) 09:00 ~ 10:00
地 点：旗山校区理工北楼601报告厅
主 办：数学与计算机科学学院, 福建省分析数学及应用重点实验室, 数学研究中心
参加对象：相关专业教师和学生
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.
专家简介：南开大学统计研究院教授，博士生导师，副院长，教育部长江特聘教授。现兼任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、天津市现场统计研究会理事长；同时兼任《数理统计与管理》副主编，《数学进展》编委，《统计信息论坛》编委。
