Arbeitspapier
Bayesian clustering for row effects models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Quaderni di Dipartimento - EPMQ ; No. 187
Klassifikation
Wirtschaft
Thema
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
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
Ereignis
Geistige Schöpfung
(wer)
Tarantola, Claudia
Consonni, Guido
Dallaportas, Petros
Consonni, Guido
Dallaportas, Petros
Ereignis
Veröffentlichung
(wer)
Università degli Studi di Pavia, Dipartimento di Economia Politica e Metodi Quantitativi (EPMQ)
(wo)
Pavia
(wann)
2006
- Handle
- Letzte Aktualisierung
-
12.07.2024, 13:20 MESZ
Objekttyp
- Arbeitspapier
Beteiligte
- Tarantola, Claudia
- Consonni, Guido
- Dallaportas, Petros
- Università degli Studi di Pavia, Dipartimento di Economia Politica e Metodi Quantitativi (EPMQ)
Entstanden
- 2006
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