speaker: doctor Lu Jun (Zhejiang Gongshang University)

time: 9:00-10:00 Sep 27, 2019

place: Lecture hall B304 in the school of mathematics and statistics

Abstract:

In this paper, we propose a groupwise dimension reduction adaptive-to-model test for conditional independence. The novel test is asymptotically normal distributed under the null hypothesis. Unlike other locally smoothing nonparametric tests for conditional independence, it behaves like a locally smoothing test as if the number of covariates was just the dimension of central subspace under the null hypothesis, which is less than that under the null hypothesis, and it can detect local alternative hypotheses distinct from the null hypothesis at the rate that is only related to the dimension of central subspace under the null hypothesis. Therefore, the curse of dimensionality is largely alleviated. To achieve the above goal, we also proposed groupwise least square estimation for the groupwise central subspace.