TY - GEN
T1 - Bayesian semiparametric symmetric models for binary data
AU - Diniz, Marcio Augusto
AU - de Bragança Pereira, Carlos Alberto
AU - Polpo, Adriano
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - This work proposes a general Bayesian semiparametric model for binary data. Symmetric prior probability curves as an extension for discussed ideas from Basu and Mukhopadhyay (Generalized Linear Models:ABayesian Perspective, pp. 231–241, 1998) are considered using the blocked Gibbs sampler, which is more general than the Polya urn Gibbs sampler. The Bayesian semiparametric approach allows us to incorporate uncertainty around the F distribution of the latent data and to model heavy-tailed or light-tailed distributions. In particular, the Bayesian semiparametric logistic model is introduced, which enables one to elicit prior distributions for regression coefficients from information about odds ratios; this is quite interesting in applied research. Then, this framework opens several possibilities to deal with binary data in the Bayesian perspective.
AB - This work proposes a general Bayesian semiparametric model for binary data. Symmetric prior probability curves as an extension for discussed ideas from Basu and Mukhopadhyay (Generalized Linear Models:ABayesian Perspective, pp. 231–241, 1998) are considered using the blocked Gibbs sampler, which is more general than the Polya urn Gibbs sampler. The Bayesian semiparametric approach allows us to incorporate uncertainty around the F distribution of the latent data and to model heavy-tailed or light-tailed distributions. In particular, the Bayesian semiparametric logistic model is introduced, which enables one to elicit prior distributions for regression coefficients from information about odds ratios; this is quite interesting in applied research. Then, this framework opens several possibilities to deal with binary data in the Bayesian perspective.
UR - https://www.scopus.com/pages/publications/84924070868
U2 - 10.1007/978-3-319-12454-4_27
DO - 10.1007/978-3-319-12454-4_27
M3 - Conference contribution
AN - SCOPUS:84924070868
T3 - Springer Proceedings in Mathematics and Statistics
SP - 323
EP - 335
BT - Interdisciplinary Bayesian Statistics, EBEB 2014
A2 - Polpo, Adriano
A2 - Louzada, Francisco
A2 - Lauretto, Marcelo
A2 - Stern, Julio Michael
A2 - Rifo, Laura Letícia Ramos
PB - Springer New York LLC
T2 - 12th Brazilian Meeting on Bayesian Statistics, EBEB 2014
Y2 - 10 March 2014 through 14 March 2014
ER -