Improved Regression Calibration

Anders Skrondal () and Jouni Kuha

Psychometrika, 2012, vol. 77, issue 4, 649-669

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. 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. Copyright The Psychometric Society 2012

Keywords: covariate measurement error; measurement model; generalized linear model; pseudo maximum likelihood estimation; regression calibration (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s11336-012-9285-1

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