Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources

Nilanjan Chatterjee, Yi Hau Chen, Paige Maas, Raymond J. Carroll

Research output: Contribution to journalArticlepeer-review

Abstract

Information from various public and private data sources of extremely large sample sizes are now increasingly available for research purposes. Statistical methods are needed for using information from such big data sources while analyzing data from individual studies that may collect more detailed information required for addressing specific hypotheses of interest. In this article, we consider the problem of building regression models based on individual-level data from an “internal” study while using summary-level information, such as information on parameters for reduced models, from an “external” big data source. We identify a set of very general constraints that link internal and external models. These constraints are used to develop a framework for semiparametric maximum likelihood inference that allows the distribution of covariates to be estimated using either the internal sample or an external reference sample. We develop extensions for handling complex stratified sampling designs, such as case-control sampling, for the internal study. Asymptotic theory and variance estimators are developed for each case. We use simulation studies and a real data application to assess the performance of the proposed methods in contrast to the generalized regression calibration methodology that is popular in the sample survey literature. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)107-117
Number of pages11
JournalJournal of the American Statistical Association
Volume111
Issue number513
DOIs
StatePublished - Jan 2 2016

Keywords

  • Case-control study
  • Empirical likelihood
  • Generalized regression estimator
  • Misspecified model
  • Profile-likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources'. Together they form a unique fingerprint.

Cite this