Model averaging for semiparametric additive partial linear models

Abstract

To improve the prediction accuracy of semiparametric additive partial linear models (APLM) and the coverage probability of confidence intervals of the parameters of interest, we explore a focused information criterion for model selection among ALPM after we estimate the nonparametric functions by the polynomial spline smoothing, and introduce a general model average estimator. The major advantage of the proposed procedures is that iterative backfitting implementation is avoided, which thus results in gains in computational simplicity. The resulting estimators are shown to be asymptotically normal. A simulation study and a real data analysis are presented for illustrations.