CAD Seminar Series:Pei Wang, Sufficient Dimension Reduction and Variable Selection by Feature Filter

Warning Icon This event is in the past.

When:
November 13, 2024
2:30 p.m. to 3:30 p.m.
Where:
Event category: Seminar
Virtual
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
 
 
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.

Contact

Rohini Kumar
rohini.kumar@wayne.edu

Cost

Free
November 2024
SU M TU W TH F SA
272829303112
3456789
10111213141516
17181920212223
24252627282930