Penalized minimum average variance estimation

Abstract

For simultaneous dimension reduction and variable selection for general regression models, including the multi-index model as a special case, we propose a penalized minimum average variance estimation method, combining the ideas of minimum average variance estimation in dimension reduction and regularization in variable selection. The resulting estimator can be found in a computationally efficient manner. Under mild conditions, the new method can consistently select all relevant predictors and has the oracle property. Simulations and a data example demonstrate the effectiveness and efficiency of the proposed method.