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Bayesian hierarchical classes analysis

Iwin Leenen; Iven van Mechelen; Andrew E. Gelman; Stijn de Knop

Title:
Bayesian hierarchical classes analysis
Author(s):
Leenen, Iwin
Mechelen, Iven van
Gelman, Andrew E.
Knop, Stijn de
Date:
Type:
Articles
Department:
Statistics
Permanent URL:
Notes:
Psychometrika, vol. 73, no. 1 (March 2008), pp. 39-64.
Abstract:
Hierarchical classes models are models for N-way N-mode data that represent the association among the N modes and simultaneously yield, for each mode, a hierarchical classification of its elements. In this paper we present a stochastic extension of the hierarchical classes model for two-way two-mode binary data. In line with the original model, the new probabilistic extension still represents both the association among the two modes and the hierarchical classifications. A fully Bayesian method for fitting the new model is presented and evaluated in a simulation study. Furthermore, we propose tools for model selection and model checking based on Bayes factors and posterior predictive checks. We illustrate the advantages of the new approach with applications in the domain of the psychology of choice and psychiatric diagnosis.
Subject(s):
Statistics
Publisher DOI:
http://dx.doi.org/10.1007/s11336-007-9038-8
Item views:
174
Metadata:
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