A0765
Title: KOO approach for scalable variable selection problem in large-dimensional regression
Authors: Jiang Hu - Northeast Normal University (China) [presenting]
Abstract: An important issue in many multivariate regression problems is to eliminate candidate predictors with null predictor vectors. In a large-dimensional (LD) setting where the numbers of responses and predictors are large, model selection encounters the scalability challenge. Knock-one-out (KOO) statistics hold promise to meet this challenge. The almost sure limits and the central limit theorem of the KOO statistics are derived under the LD setting and mild distributional assumptions (finite fourth moments) of the errors. These theoretical results guarantee the strong consistency of a subset selection rule based on the KOO statistics with a general threshold. To enhance the robustness of the selection rule, a bootstrap threshold is also proposed for the KOO approach. Simulation results support the conclusions and demonstrate the selection probabilities by the KOO approach, with the bootstrap threshold outperforming the methods using the Akaike information threshold, Bayesian information threshold, and Mallow's Cp threshold. The proposed KOO approach is compared with those based on information threshold to a chemometrics dataset and a yeast cell-cycle dataset, which suggests the proposed method identifies useful models.
