Date: 2024-03-22

Time: 15:30-16:30 (Montreal time)

Location: Online, retransmitted in Burnside 1104

https://mcgill.zoom.us/j/81895414756

Meeting ID: 818 9541 4756

Passcode: None

Abstract:

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.

Speaker

Qiang Heng is currently a postdoctoral scholar at UCLA computational medicine, supervised by Kenneth L. Lange. He received his Ph.D. degree in statistics from North Carolina State University in 2023. He is interested in computational approaches in statistics. numerical methods, and statistical genetics.