讲座题目：A scalable nonparametric specification testing in massive data（大数据非参数模型识别）

主 讲 人：南开大学王兆军教授

时 间：2016年12月16日（周五）13:30-14:30

地 点：6号学院楼415教室

主 持 人：罗季副教授

主办单位：数据科学学院

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：

南开大学统计研究院教授，教育部长江特聘教授，国务院学位委员会统计学科评议组成员，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院理事长，天津市统计学副会长。主要研究方向为统计过程控制(SPC)、非(半)参数回归、降维、高维数据分析、变点。主持国家级课题6项，曾获天津市自然科学一等奖、全国百篇优博指导教师。

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