报告时间：2024年3月12日（周二）10：00-11：00
报告地点：明德主楼1016
报告人:郭旭
报告主题：Model-free variable importance detection with machine learning methods
报告摘要：
In this paper, we propose a new procedure to detect variable importance in a model-free framework. Flexible machine learning methods are adopted to estimate unknown functions. Under null hypothesis, our proposed test statistic converges to standard chi-squared distribution. While under local alternative hypotheses, it converges to non-central chi-square distribution. It has non-trivial power against the local alternative hypothesis which converges to the null at root-n rate. We also extend our procedure to test conditional independence. Asymptotic properties are also developed. Numerical studies and a real data example are conducted to illustrate the performance of our proposed test statistic.
报告人简介：
郭旭博士，现为北京师范大学统计学院教授，博士生导师。郭老师一直从事回归分析中复杂假设检验的理论方法及应用研究，近年来旨在对高维数据发展适当有效的检验方法。部分成果发表在JRSSB, JASA，Biometrika和JOE。担任《应用概率统计》杂志第十届编委。现主持国家自然科学基金优秀青年基金。曾荣获北师大第十一届“最受本科生欢迎的十佳教师”，北师大第十八届青教赛一等奖和北京市第十三届青教赛三等奖。