报告题目：Distance-based regression analysis for measuring associations
报 告 人：李启寨 研究员 中国科学院
报告时间：2021年4月24日 下午 15:30-16:30
报告地点：数学楼 第二报告厅
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：
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
报告人简介：
李启寨，中国科学院数学与系统科学研究院 研究员，2001年于中国科学技术大学获学士学位，2006年于中国科学院研究生院获博士学位。2006年7月至今在中国科学院数学与系统科学研究院工作， 2006-2010年任助理研究员，2010-2015任副研究员，2015至今任研究员。研究方向：生物统计、数理统计。发表SCI论文近100篇，曾获美国统计学会会士(ASA Fellow),国际统计学会推选会员(ISI Elected Member),中国工业与应用数学学会优秀青年学者奖等。曾主持基金委优青、面上等项目；现任中国数学会常务理事等。
