Parameter estimation of multiple item response profile model.
Br J Math Stat Psychol
; 65(3): 438-66, 2012 Nov.
Article
en En
| MEDLINE
| ID: mdl-22070786
ABSTRACT
Multiple item response profile (MIRP) models are models with crossed fixed and random effects. At least one between-person factor is crossed with at least one within-person factor, and the persons nested within the levels of the between-person factor are crossed with the items within levels of the within-person factor. Maximum likelihood estimation (MLE) of models for binary data with crossed random effects is challenging. This is because the marginal likelihood does not have a closed form, so that MLE requires numerical or Monte Carlo integration. In addition, the multidimensional structure of MIRPs makes the estimation complex. In this paper, three different estimation methods to meet these challenges are described the Laplace approximation to the integrand; hierarchical Bayesian analysis, a simulation-based method; and an alternating imputation posterior with adaptive quadrature as the approximation to the integral. In addition, this paper discusses the advantages and disadvantages of these three estimation methods for MIRPs. The three algorithms are compared in a real data application and a simulation study was also done to compare their behaviour.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Teorema de Bayes
/
Modelos Psicológicos
Tipo de estudio:
Diagnostic_studies
/
Health_economic_evaluation
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Prognostic_studies
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Risk_factors_studies
Límite:
Female
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Humans
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Male
Idioma:
En
Revista:
Br J Math Stat Psychol
Año:
2012
Tipo del documento:
Article
País de afiliación:
Estados Unidos