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关于大数据的一种可扩展性非参数化检验方法

来源:  作者:张伶     日期:2017/3/21 0:00:00   点击数:2037  
 

“创源”大讲堂研究生学术讲座

主讲人:  王兆军 教授

讲座题目:关于大数据的一种可扩展性非参数化检验方法(A scalable nonparametric specification testing in massive data

讲座时间:2017324日星期五上午10:30-11:30

讲座地点:犀浦校区数学学院报告厅X2511

主讲人简介:

王兆军,南开大学统计研究院教授,教育部长江特聘教授,国务院学位委员会第七届学科评议组成员(统计学),国家统计专家咨询委员会成员。中国现场统计研究会副理事长,中国统计学会常务理事,天津市现场统计研究会理事长,天津市统计学副会长。曾获全国百篇优博指导教师和天津市自然科学一等奖。

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 detectionproperties. Its advantage relative to existing methods is also demonstrated in numerical simulations and a data illustration.

主办:研究生院

承办:数学学院