報告題目：Distance-based regression analysis for measuring associations
報 告 人：李啟寨 研究員 中國科學院
報告時間：2021年4月24日 下午 15:30-16:30
報告地點：數學樓 第二報告廳
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：
Distance-based regression model, as a nonparametric multivariate method, has been widely used to detect the association between variations in a distance or dissimilarity matrix for outcomes and predictor variables of interest. Based on it, a pseudo-F statistic which partitions the variation in distance matrices is often constructed to achieve the aim. To the best of our knowledge, the statistical properties of the pseudo-F statistic has not yet been well established in the literature. To fill this gap, we study the asymptotic null distribution of the pseudo-F statistic and show that it is asymptotically equivalent to a mixture of chi-squared random variables. Given that the pseudo-F test statistic has unsatisfactory power when the correlations of the response variables are large, we propose a square-root F-type test statistic which replaces the similarity matric with its square root. The asymptotic null distribution of the new test statistic and power of both tests are also investigated. Simulation studies are conducted to validate the asymptotic distributions of the tests and demonstrate that the proposed test has more robust power than the pseudo-$F$ test. Both test statistics are exemplified with a gene expression dataset for a prostate cancer pathway
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，2001年于中國科學技術大學獲學士學位，2006年于中國科學院研究生院獲博士學位。2006年7月至今在中國科學院數學與系統科學研究院工作， 2006-2010年任助理研究員，2010-2015任副研究員，2015至今任研究員。研究方向：生物統計、數理統計。發表SCI論文近100篇，曾獲美國統計學會會士(ASA Fellow),國際統計學會推選會員(ISI Elected Member),中國工業與應用數學學會優秀青年學者獎等。曾主持基金委優青、面上等項目；現任中國數學會常務理事等。
