Structural Measurement Error Models
Structural Measurement Error Models is a class of Bayesian measurement error models that explicitly incorporate assumptions about the underlying distribution of the true, unobserved covariates, distinguishing them from functional measurement error models by treating latent variables as random effects with a specified parametric or nonparametric prior structure.
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Herbert W. Marsh, Oliver Lüdtke, Alexander Robitzsch et al. · Multivariate Behavioral Research · 2009 · 473 citations
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North Carolina State University
Raleigh, United States
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8 publications
471 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
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7 publications
601 citations

Ghent University
Ghent, Belgium
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3 publications
40 citations
University of South Carolina
Columbia, United States
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3 publications
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Institute of Statistical Science, Academia Sinica
Taipei, Taiwan
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21 citations

University of Amsterdam
Amsterdam, The Netherlands
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3 publications
160 citations
Ludwig-Maximilians-Universität München
Munich, Germany
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34 citations
Northwestern University
Evanston, United States
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