报告题目：An adaptive lack of t test for bigdata
报 告 人：王兆军教授（南开大学）
New technological advancements combinedwith powerful computer hardware and high-speed network make big data available.The massive sample size of big data introduces unique computational challengeson scalability and storage of statistical methods. In this paper, we focus onthe lack of t test of parametric regression models under the framework of bigdata. We develop a computationally feasible testing approach via integratingthe divide and conquer algorithm into a powerful nonparametric test statistic.Our theory results show that under mild conditions the asymptotic nulldistribution of the proposed test is standard normal. Furthermore, the proposedtest benets from the use of data-driven bandwidth procedure and thus possessescertain adaptive property. Simulation studies show that the proposed method hassatisfactory performances, and it is illustrated with an analysis of an airlinedata.