NEW IMPROVED CRITERION FOR MODEL SELECTION IN SPARSE HIGH-DIMENSIONAL LINEAR REGRESSION MODELS

Extended Bayesian information criterion (EBIC) and extended Fisher information criterion (EFIC) are two popular criteria for model selection in sparse high-dimensional linear regression models. However, EBIC is inconsistent in scenarios when the signal-to-noise-ratio (SNR) is high but the sample size is small, and EFIC is not invariant to data scaling, which effects its performance under different signal and noise statistics. In this paper, we present a refined criterion called EBIC$_\text{R}$ where the 'R' stands for robust. EBIC$_\text{R}$ is an improved version of EBIC and EFIC. It is scale-invariant and a consistent estimator of the true model as the sample size grows large and/or when the SNR tends to infinity. The performance of EBIC$_\text{R}$ is compared to existing methods such as EBIC, EFIC and multi-beta-test (MBT). Simulation results indicate that the performance of EBIC$_\text{R}$ in identifying the true model is either at par or superior to that of the other considered methods.