VARIABLE SELECTION VIA PARTIAL CORRELATION

A partial correlation-based variable selection method was proposed for normal linear regression models by Biihlmann, Kalisch and Maathuis (2010) as an alternative to regularization methods for variable selection. This paper addresses issues related to (a) whether the method is sensitive to the normality assumption, and (b) whether the method is valid when the dimension of predictor increases at an exponential rate in the sample size. To address (a), we study the method for elliptical linear regression models. Our finding indicates that the original proposal can lead to inferior performance when the marginal kurtosis of predictor is not close to that of normal distribution, and simulation results confirm this. To ensure the superior performance of the partial correlation-based variable selection procedure, we propose a thresholded partial correlation (TPC) approach to select significant variables in linear regression models. We establish the selection consistency of the TPC in the presence of ultrahigh dimensional predictors. Since the TPC procedure includes the original proposal as a special case, our results address the issue (b) directly. As a by-product, the sure screening property of the first step of TPC is obtained. Numerical examples illustrate that the TPC is comparable to the commonly-used regularization methods for variable selection.