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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Bibliographic Details
Published in:Psychometrika 2012-10, Vol.77 (4), p.649-669
Main Authors: Skrondal, Anders, Kuha, Jouni
Format: Article
Language:English
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
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Summary: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.
ISSN:0033-3123
1860-0980
DOI:10.1007/s11336-012-9285-1