Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-level Information from External Big Data Sources.

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 utilizing 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 utilizing 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 likel

SUBMITTER: Chatterjee N 

PROVIDER: S-EPMC4994914 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature