“創(chuàng)源”大講堂研究生學(xué)術(shù)講座

主講人：  王兆軍 教授

講座題目：關(guān)于大數(shù)據(jù)的一種可擴(kuò)展性非參數(shù)化檢驗(yàn)方法（A scalable nonparametric specification testing in massive data）

講座時間：2017年3月24日星期五上午10:30-11:30

講座地點(diǎn)：犀浦校區(qū)kaiyun開云官方網(wǎng)站報告廳X2511

主講人簡介：

王兆軍，南開大學(xué)統(tǒng)計研究院教授，教育部長江特聘教授，國務(wù)院學(xué)位委員會第七屆學(xué)科評議組成員（統(tǒng)計學(xué)），國家統(tǒng)計專家咨詢委員會成員。中國現(xiàn)場統(tǒng)計研究會副理事長，中國統(tǒng)計學(xué)會常務(wù)理事，天津市現(xiàn)場統(tǒng)計研究會理事長，天津市統(tǒng)計學(xué)副會長。曾獲全國百篇優(yōu)博指導(dǎo)教師和天津市自然科學(xué)一等獎。

報告摘要：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.

主辦：研究生院

承辦：kaiyun開云官方網(wǎng)站