A1946
Title: Variable selection for PFC Models in high dimensions
Authors: Seungchul Baek - University of Maryland (United States) [presenting]
Junyong Park - Seoul National University (Korea, South)
Hoyoung Park - Seoul National University (Korea, South)
Abstract: Sufficient dimension reduction (SDR) is an effective way to detect nonlinear relationships between response variables and covariates by reducing the dimensionality of covariates without information loss. The principal fitted component (PFC) model is a way to implement SDR using some class of basis functions, however, the PFC model is not efficient when there are many irrelevant or noisy covariates. There have been a few studies on the selection of variables in the PFC model via penalized regression or sequential likelihood ratio test. A novel variable selection technique in the PFC model has been proposed by incorporating a recent development in multiple testing such as mirror statistics and random data splitting. It is highlighted how a mirror statistic is constructed in the PFC model using the idea of projection of coefficients to the other space generated from data splitting. The proposed method is superior to some existing methods in terms of false discovery rate (FDR) control and applicability to high-dimensional cases. In particular, the proposed method outperforms other methods as the number of covariates tends to be getting larger, which would be appealing in high dimensional data analysis.
