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A MCMC-Method for Models with Continuous Latent Responses

Published online by Cambridge University Press:  01 January 2025

Gunter Maris  and
Eric Maris
Gunter Maris*
Affiliation:
Department of Mathematical Psychology, Nijmegen Institute for Cognition and Information (NICI), University of Nijmegen
Eric Maris
Affiliation:
Department of Mathematical Psychology, Nijmegen Institute for Cognition and Information (NICI), University of Nijmegen
*
Requests for reprints should be sent to Gunter Maris, Department of Mathematical Psychology, Nijmegen Institute for Cognition and Information, University of Nijmegen, P.O. Box 9104, 6500 HE Nijmegen, THE NETHERLANDS. E-Mail: [email protected]
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Abstract

This paper introduces a new technique for estimating the parameters of models with continuous latent data. Using the Rasch model as an example, it is shown that existing Bayesian techniques for parameter estimation, such as the Gibbs sampler, are not always easy to implement. Then, a new sampling-based Bayesian technique, called the DA-T-Gibbs sampler, is introduced. The DA-T-Gibbs sampler relies on the particular latent data structure of latent response models to simplify the computations involved in parameter estimation.

Keywords

MCMC methodsGibbs sampling
Type
Articles
Information
Psychometrika , Volume 67 , Issue 3 , September 2002 , pp. 335 - 350
Copyright
Copyright © 2002 The Psychometric Society

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Footnotes

This research was supported by the Dutch National Research Council (NWO) (grant number 575-30-001).

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