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Improved Regression Calibration
The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form, which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We...
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Published in: | Psychometrika 2012-10, Vol.77 (4), p.649-669 |
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description | The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form, which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We propose an improved regression calibration approach, a general pseudo maximum likelihood estimation method based on a conveniently decomposed form of the likelihood. It is both consistent and computationally efficient, and produces point estimates and estimated standard errors which are practically identical to those obtained by maximum likelihood. Simulations suggest that improved regression calibration, which is easy to implement in standard software, works well in a range of situations. |
doi_str_mv | 10.1007/s11336-012-9285-1 |
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A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We propose an improved regression calibration approach, a general pseudo maximum likelihood estimation method based on a conveniently decomposed form of the likelihood. It is both consistent and computationally efficient, and produces point estimates and estimated standard errors which are practically identical to those obtained by maximum likelihood. Simulations suggest that improved regression calibration, which is easy to implement in standard software, works well in a range of situations.</description><subject>Assessment</subject><subject>Behavioral Science and Psychology</subject><subject>Biological and medical sciences</subject><subject>Calibration</subject><subject>Computation</subject><subject>Computer Simulation</subject><subject>Computer Software</subject><subject>Data Analysis</subject><subject>Epidemiology</subject><subject>Error of Measurement</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Generalized linear models</subject><subject>Humanities</subject><subject>Indexing in process</subject><subject>Law</subject><subject>Maximum Likelihood Statistics</subject><subject>Psychology</subject><subject>Psychology. Psychoanalysis. Psychiatry</subject><subject>Psychology. Psychophysiology</subject><subject>Psychometrics</subject><subject>Psychometrics. Statistics. 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Psychology</topic><topic>Generalized linear models</topic><topic>Humanities</topic><topic>Indexing in process</topic><topic>Law</topic><topic>Maximum Likelihood Statistics</topic><topic>Psychology</topic><topic>Psychology. Psychoanalysis. Psychiatry</topic><topic>Psychology. Psychophysiology</topic><topic>Psychometrics</topic><topic>Psychometrics. Statistics. Methodology</topic><topic>Public health</topic><topic>Regression (Statistics)</topic><topic>Statistical Theory and Methods</topic><topic>Statistics for Social Sciences</topic><topic>Statistics. Mathematics</topic><topic>Testing and Evaluation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Skrondal, Anders</creatorcontrib><creatorcontrib>Kuha, Jouni</creatorcontrib><collection>ERIC</collection><collection>ERIC (Ovid)</collection><collection>ERIC</collection><collection>ERIC</collection><collection>ERIC (Legacy Platform)</collection><collection>ERIC( SilverPlatter )</collection><collection>ERIC</collection><collection>ERIC PlusText (Legacy Platform)</collection><collection>Education Resources Information Center (ERIC)</collection><collection>ERIC</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection【Remote access available】</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>ABI/INFORM Collection (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Education Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Social Science Premium Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>Education Collection</collection><collection>ProQuest Central</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest ABI/INFORM Global</collection><collection>Education Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Psychology Database (ProQuest)</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Education</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><jtitle>Psychometrika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Skrondal, Anders</au><au>Kuha, Jouni</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>EJ983213</ericid><atitle>Improved Regression Calibration</atitle><jtitle>Psychometrika</jtitle><stitle>Psychometrika</stitle><date>2012-10-01</date><risdate>2012</risdate><volume>77</volume><issue>4</issue><spage>649</spage><epage>669</epage><pages>649-669</pages><issn>0033-3123</issn><eissn>1860-0980</eissn><coden>PSMTA2</coden><abstract>The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form, which makes maximum likelihood estimation taxing. 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subjects | Assessment Behavioral Science and Psychology Biological and medical sciences Calibration Computation Computer Simulation Computer Software Data Analysis Epidemiology Error of Measurement Fundamental and applied biological sciences. Psychology Generalized linear models Humanities Indexing in process Law Maximum Likelihood Statistics Psychology Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Psychometrics Psychometrics. Statistics. Methodology Public health Regression (Statistics) Statistical Theory and Methods Statistics for Social Sciences Statistics. Mathematics Testing and Evaluation |
title | Improved Regression Calibration |
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