報告時間：2024年11月15日（星期五）9:30-10:30
報告地點：翡翠湖校區科教樓B座1710室
報 告 人：郭旭 教授
工作單位：北京師范大學
舉辦單位：數學學院
報告簡介：
In this paper, we investigate variable importance testing problem in a model-free framework. Some remarkable procedures are developed recently. Despite their success, existing procedures suffer from a significant limitation, that is, they generally require larger training sample and do not have the fastest possible convergence rate under alternative hypothesis. In this paper, we propose a new procedure to test variable importance. 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 the fastest possible rate. We also extend our procedure to test conditional independence. Asymptotic properties are also developed. Numerical studies and two real data examples are conducted to illustrate the performance of our proposed test statistic.
報告人簡介：
郭旭，博士，現為北京師范大學統計學院教授，博士生導師。長期從事回歸分析中復雜假設檢驗的理論方法及應用研究，近年來旨在對高維數據發展適當有效的檢驗方法。部分成果發表在JRSSB, JASA，Biometrika，JOE和NeurIPS。現主持國家自然科學基金優秀青年基金。曾榮獲北師大第十一屆“最受本科生歡迎的十佳教師”，北師大第十八屆青教賽一等獎和北京市第十三屆青教賽三等獎。
