A1268
Title: Robust screening of correlated features via forward regression
Authors: Xuejun Jiang - Southern University of Science and Technology (China) [presenting]
Abstract: Forward regression is a crucial methodology for automatically identifying important predictors from a large pool of potential covariates. In contexts with moderate predictor correlation, forward selection techniques can achieve screening consistency. However, this property gradually becomes invalid in the presence of substantially correlated variables, especially in high-dimensional datasets where strong correlations exist among predictors. This dilemma is encountered by other model selection methods in literature as well. To address these challenges, we propose a novel decorrelated forward (DF) selection framework for generalized mean regression models, including prevalent models, such as linear, logistic, poisson, and quasi likelihood. We also develop a thresholding DF (T-DF) algorithm that provides a principled stopping rule for the forward-searching process. Theoretically, we establish the screening consistency of T-DF selection and determine the upper bound of the selected submodel's size. Simulations and two real data applications show the outstanding performance of our method compared with some existing model selection methods.
