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

Chatterjee, N; Chen, YH; Maas, P; Carroll, RJ

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

Chatterjee, N Chen, YH Maas, P Carroll, RJ

Permalink

Publication Type:: Journal Article
Citation:: Journal of the American Statistical Association, 2016, 111 (513), pp. 107 - 117
Issue Date:: 2016-01-02

Closed Access

	Filename	Description	Size
	Constrained Maximum Likelihood Estimation for Model Calibration Using Summary Level Information From External Big Data Sources.pdf	Published Version	520.49 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chatterjee, N	en_US
dc.contributor.author	Chen, YH	en_US
dc.contributor.author	Maas, P	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.date.issued	2016-01-02	en_US
dc.identifier.citation	Journal of the American Statistical Association, 2016, 111 (513), pp. 107 - 117	en_US
dc.identifier.issn	0162-1459	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122358
dc.description.abstract	© 2016 American Statistical Association. 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.	en_US
dc.relation.ispartof	Journal of the American Statistical Association	en_US
dc.relation.isbasedon	10.1080/01621459.2015.1123157	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources	en_US
dc.type	Journal Article
utslib.citation.volume	513	en_US
utslib.citation.volume	111	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1603 Demography	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	513	en_US
pubs.publication-status	Published	en_US
pubs.volume	111	en_US

Abstract:

© 2016 American Statistical Association. 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/122358