報告題目：Kernel-based independence tests for high-dimensional response data
報 告 人：李啟寨 研究員
所在單位：中科院數學與系統科學研究院
報告時間：2022年7月11日 星期一 上午9:30-10:30
報告地點：騰訊會議：154-726-320 會議密碼：2022
報告摘要： Testing independence between high-dimensional response variable and some covariates is frequently encountered in statistical applications nowadays, and the kernelbased methods have been developed recently. However, the traditional kernel-based methods may suffer from substantial power loss under the situations with moderate to high correlations among responses. In this work, we first propose a set of kernel-based independence tests on the basis of angles between two reproducing kernel Hilbert spaces, and obtain their asymptotical null distributions. Then, we construct two tests including maximal kernel-based independence test (MKIT) and maximin efficient robust test (MERT). Under some regular conditions, we prove that MKIT and MERT asymptotically follow extreme-value type I-Gumbel distribution and normal distribution, respectively. The powers of MKIT and MERT are also investigated. Extensive simulation studies show that MKIT and MERT are more powerful and robust than many existing procedures over a wide range of situations. Applications to heterogeneous stock mice and prostate cancer pathway data ulteriorly demonstrate the performances of proposed methods.
報告人簡介: 李啟寨，中國科學院數學與系統科學研究院研究員，中國科學院大學教授，美國統計學會會士，國際統計學會推選會員，2001年本科畢業于中國科技大學，2006年博士畢業于中國科學院研究生院；研究方向：生物醫學統計等；發表及接收發表SCI論文100餘篇。現任中國數學會常務理事、中國現場統計研究會常務理事等。
