Bayesian clustering for row effects models - Claudia Tarantola; Guido Consonni; Petros Dallaportas (Università degli Studi di Pavia, Dipartimento di Economia Politica e Metodi Quantitativi (EPMQ), 2006)
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 enhancing 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.
Keywords: 330; Clustering; Contingency table; Log-linear model; Markov Chain Monte Carlo; Mixture of Dirichlet process prior; Partition; Product partition model; Row effects model; Clusteranalyse; Qualitatives Verfahren; Logit-Modell; Markovscher Prozess; Bayes-Statistik; Statistische Methode; Theorie