Title: Transformed variable selection in sufficient dimension reduction
Authors: Yuexiao Dong - Temple University (United States) [presenting]
Abstract: Variable transformation with sufficient dimension reduction are combined to achieve model-free variable selection. Existing model-free variable selection methods via sufficient dimension reduction requires a critical assumption that the predictor distribution is elliptically contoured. We suggest a nonparametric variable transformation method after which the predictors become normal. Variable selection is then performed based on the marginally transformed predictors. Asymptotic theory is established to support the proposed method. The desirable variable selection performance of the proposed method is demonstrated through simulation studies and a real data analysis.