http://www.cnr.it/ontology/cnr/individuo/prodotto/ID183489
Algebraic Bayesian analysis of contingency tables with possibly zero-probability cells. (Articolo in rivista)
- Type
- Label
- Algebraic Bayesian analysis of contingency tables with possibly zero-probability cells. (Articolo in rivista) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Guido Consonni;Giovanni Pistone (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www3.stat.sinica.edu.tw/statistica/oldpdf/A17n45.pdf (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Università di Pavia; Politecnico di Torino (literal)
- Titolo
- Algebraic Bayesian analysis of contingency tables with possibly zero-probability cells. (literal)
- Abstract
- In this paper we consider a Bayesian analysis of contingency tables allowing
for the possibility that cells may have probability zero. In this sense we depart
from standard log-linear modeling that implicitly assumes a positivity constraint.
Our approach leads us to consider mixture models for contingency tables, where the
components of the mixture, which we call model-instances, have distinct support.
We rely on ideas from polynomial algebra in order to identify the various model
instances. We also provide a method to assign prior probabilities to each instance of
the model, and we describe methods for constructing priors on the parameter space
of each instance. We illustrate our methodology through a 5 × 2 table involving
two structural zeros, as well as a zero count. The results we obtain show that our
analysis may lead to conclusions that are substantively different from those that
would obtain in a standard framework, wherein the possibility of zero-probability
cells is not explicitly accounted for. (literal)
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