报告简介  Abstract

In this paper, we propose a nonparametric independence test based on mutual information. Distinguished from the previous work, we estimate the mutual information in a conditional density form, whose dimension could be reduced to 1 with new projection methods. The optimal projection direction, which we name as maximum unit direction, is estimated by maximizing a penalized mutual information. An independence test is later on carried out via the newly estimated mutual information and is shown to be insensitive to the dimensions. The test is consistent against fixed alternatives, and can detect local alternatives at a fast rate almost as if the model was univariate. Numerical results indicate that the test is more powerful compared with other existing independence tests, especially when the sample size is small or the dimension is large.

嘉宾简介  About the Speaker

郭旭博士，现任北京师范大学统计学院副教授，博士生导师。郭旭博士于 2014 年获得香港浸会大学博士学位，2018 年 9 月至 2020 年 2 月作为助理研究教授 (Assistant Research Professor) 访问美国宾州州立大学统计系。郭旭博士一直从事模型设定检验、高维数据分析和半参数回归分析等方面的研究，在包括JRSSB，Biometrika，Science China-Mathematics等杂志上发表论了一系列的研究论文。

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