Improving prediction in M-estimation by integrating external information from heterogeneous populations

Abstract

A novel approach to improve prediction and inference in M-estimation by integrating external information from heterogeneous populations is proposed. Our method leverages joint asymptotics to combine estimates from external and internal datasets, where the external dataset provides auxiliary information about a subset of parameters of interest. We introduce a shrinkage estimator that combines internal and external estimates under a general class of transformations that ensure consistency across populations.