CAD Seminar Series:Pei Wang, Sufficient Dimension Reduction and Variable Selection by Feature Filter
This event is in the past.
Speaker: Pei Wang, Ph.D., Bowling Green State University,
Assistant Professor, Department of Applied Statistics and Operations Research
Time: Wednesday, November 13, from 2:30 to 3:30 pm
Location for in-person participants: Virtual
Zoom link for online audience: https://wayne-edu.zoom.us/j/92845590121?pwd=CpRA5Wa5gzSMn2xiVkR2abD83O5nrH.1
Title: Sufficient Dimension Reduction and Variable Selection by Feature Filter
Abstract:
The minimum discrepancy approach proves useful in sufficient dimension reduction (SDR). In this study, we propose two novel SDR estimators based on a feature filter technique derived from the characteristic function, employing the minimum discrepancy function. In an ultra-high dimension setting with sparse assumptions, we introduce a regularization method aiming to achieve SDR and SVS (Sufficient Variable Selection) simultaneously. We establish asymptotic results and provide an estimation method for determining the structural dimension. To showcase the efficacy of our method, we conduct extensive simulations and present a real data example.