We deal with two-way contingency tables having ordered column categories.We use a row effects model wherein each interaction term is assumed to have a multiplicative form involving a row effect parameter and a fixed column score.We propose a methodology to cluster row effects in order to simplify the interaction structure and to enhance the interpretation of the model. Our method uses a product partition model with a suitable specification of the cohesion function, so that we can carry out our analysis on a collection of models of varying dimensions using a straightforward MCMC sampler. The methodology is illustrated with reference to simulated and real data sets.

Bayesian clustering for row effects models

TARANTOLA, CLAUDIA;
2008-01-01

Abstract

We deal with two-way contingency tables having ordered column categories.We use a row effects model wherein each interaction term is assumed to have a multiplicative form involving a row effect parameter and a fixed column score.We propose a methodology to cluster row effects in order to simplify the interaction structure and to enhance the interpretation of the model. Our method uses a product partition model with a suitable specification of the cohesion function, so that we can carry out our analysis on a collection of models of varying dimensions using a straightforward MCMC sampler. The methodology is illustrated with reference to simulated and real data sets.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/355926
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