【专家简介】：衡强，于2023年取得北卡州立统计学博士学位，现于加州大学洛杉矶分校从事博士后研究，研究兴趣包括统计学习方法与理论，数值优化，在统计基因组学方面的应用。研究成果发表在JCGS, Technometrics, ICLR, The American Statistician等。
【报告摘要】：This paper introduces several enhancements to the minimum covariance determinant method of outlier detection and robust estimation of means and covariances. We leverage the principal component transform to achieve dimension reduction and ultimately better analyses. Our best subset selection algorithm strategically combines statistical depth and concentration steps. To ascertain the appropriate subset size and number of principal components, we introduce a bootstrap procedure that estimates the instability of the best subset algorithm. The parameter combination exhibiting minimal instability proves ideal for the purposes of outlier detection and robust estimation. Rigorous benchmarking against prominent MCD variants showcases our approach's superior statistical performance and computational speed in high dimensions. Application to a fruit spectra data set and a cancer genomics data set illustrates our claims.
时间：2024年04月10日 14:00 – 15:30
会议地点：位育楼417
