Mixture of binomial distributions are often considered as a flexible model for count data which can account for sources of heterogeneity in the population and also as a device to deal with exchangeable binary sequences. For instance they are routinely used in a wide range of applied context such as psychological testing as well as in industrial sampling or in toxicological experiments, just to mention some of them. Different param- eterizations are presented in order to build up a convenient methodological framework for developing a default Bayesian analysis when no parametric form of the mixing distribu- tion is assumed. This approach can be exploited for estimation and prediction purposes. Also in this paper a first attempt to investigate its usefulness in model selection problems is carried out.
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|Titolo:||A canonical moment approach to the analysis of binomial mixtures|
|Data di pubblicazione:||2004|
|Appartiene alla tipologia:||02a Capitolo o Articolo|