Bayesian analysis of a correlated binomial model
Diniz, Carlos A. R. ; Tutia, Marcelo H. ; Leite, Jose G.
Braz. J. Probab. Stat., Tome 24 (2010) no. 1, p. 68-77 / Harvested from Project Euclid
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In this paper a Bayesian approach is applied to the correlated binomial model, CB(n, p, ρ), proposed by Luceño (Comput. Statist. Data Anal. 20 (1995) 511–520). The data augmentation scheme is used in order to overcome the complexity of the mixture likelihood. MCMC methods, including Gibbs sampling and Metropolis within Gibbs, are applied to estimate the posterior marginal for the probability of success p and for the correlation coefficient ρ. The sensitivity of the posterior is studied taking into account several reference priors and it is shown that the posterior characteristics appear not to be influenced by these prior distributions. The article is motivated by a study of plant selection.
Publié le : 2010-03-15
Classification:  Correlated binomial distribution,  data augmentation method,  Bayesian inference,  MCMC methods 
@article{1262271216,
     author = {Diniz, Carlos A. R. and Tutia, Marcelo H. and Leite, Jose G.},
     title = {Bayesian analysis of a correlated binomial model},
     journal = {Braz. J. Probab. Stat.},
     volume = {24},
     number = {1},
     year = {2010},
     pages = { 68-77},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1262271216}
}

Diniz, Carlos A. R.; Tutia, Marcelo H.; Leite, Jose G. Bayesian analysis of a correlated binomial model. Braz. J. Probab. Stat., Tome 24 (2010) no. 1, pp.  68-77. http://gdmltest.u-ga.fr/item/1262271216/