A partially linear framework for massive heterogeneous data

Tianqi Zhao, Guang Cheng, Han Liu

Research output: Contribution to journalArticlepeer-review

89 Scopus citations


We consider a partially linear framework for modeling massive heterogeneous data. The major goal is to extract common features across all subpopulations while exploring heterogeneity of each subpopulation. In particular, we propose an aggregation type estimator for the commonality parameter that possesses the (nonasymptotic) minimax optimal bound and asymptotic distribution as if there were no heterogeneity. This oracle result holds when the number of subpopulations does not grow too fast. 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. We also test the heterogeneity among a large number of subpopulations. All the above results require to regularize each subestimation as though it had the entire sample. Our general theory applies to the divide-and-conquer approach that is often used to deal with massive homogeneous data. A technical by-product of this paper is statistical inferences for general kernel ridge regression. Thorough numerical results are also provided to back up our theory.

Original languageEnglish (US)
Pages (from-to)1400-1437
Number of pages38
JournalAnnals of Statistics
Issue number4
StatePublished - 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Divide-and-conquer method
  • Heterogeneous data
  • Kernel ridge regression
  • Massive data
  • Partially linear model


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