A Bayesian Approach to Nonlinear Latent Variable Models Using the Gibbs Sampler and the Metropolis-Hastings Algorithm

A Bayesian Approach to Nonlinear Latent Variable Models Using the Gibbs Sampler and the Metropolis-Hastings Algorithm

Nonlinear latent variable models are specified that include quadratic forms and interactions of latent regressor variable as special cases. To estimate the parameters, the models are put in a Bayesian framework with conjugate priors for the parameters. The proposed estimation methods are illustrated...

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Veröffentlicht in:Psychometrika 1998-09, Vol.63 (3), p.271-300
Hauptverfasser: Arminger, Gerhard, Muthen, Bengt O
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Bayesian Statistics
Biological and medical sciences
Estimation (Mathematics)
Fundamental and applied biological sciences. Psychology
Gibbs Sampling
Latent Variables
Mathematical Models
Nonlinear Models
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics. Statistics. Methodology
Simulation
Statistics. Mathematics
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Beschreibung
Zusammenfassung:Nonlinear latent variable models are specified that include quadratic forms and interactions of latent regressor variable as special cases. To estimate the parameters, the models are put in a Bayesian framework with conjugate priors for the parameters. The proposed estimation methods are illustrated by two simulation studies. (SLD)
ISSN:0033-3123
1860-0980
DOI:10.1007/bf02294856