Speaker: Yiyuan She, The Florida State University

Abstract: The availability of high-dimensional data in statistical applications has created an urgent need for methodologies to pursue sparse and/or low rank models. To provide a proper amount of shrinkage, most statistical approaches use a grid search with a model comparison criterion to locate proper values of the regularization parameters. We study cross-validation for multivariate models where relevant features may lie in a low dimensional subspace. By cross-validating candidate projection-selection patterns instead of regularization parameters, we are able to link cross-validation to a class of information criteria. A scale-free rate calibration helps cross-validation achieve non-asymptotic optimality in prediction.