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  • 统计与管理学院2017年学术报告第70期

    【主 题】 An adaptive lack of t test for big data

    【报告人】 王兆军 教授

    南开大学

    【时 间】 2017年12月08日(星期五)14:00-15:00

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    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.

    邀请人】吴纯杰