Additive Partially Linear Models for Massive Heterogeneous Data

We consider an additive partially linear framework for modelling massive heterogeneous data. The major goal is to extract multiple common features simultaneously across all sub-populations while exploring heterogeneity of each sub-population. This work generalizes the partially linear framework proposed in Zhao et al. (2016), which considers only one common feature. Motivated by Zhao et al. (2016), we propose an aggregation type of estimators for the commonality parameters that possess the asymptotic optimal bounds and the asymptotic distributions as if there were no heterogeneity. This oracle result holds when the number of sub-populations does not grow too fast and the tuning parameters are selected carefully. A plug-in estimator for the heterogeneity parameter is further constructed, and shown to possess the asymptotic distribution as if the commonality information were available. The performance of the proposed methods is evaluated via simulation studies and an application to the Medicare Provider Utilization and Payment data.