 Correlated binomial regression modelsRubiane M. Pires and Carlos A.R. Diniz Computational Statistics & Data Analysis, 2012, vol. 56, issue 8, 2513-2525 Abstract: In this paper, a class of correlated binomial regression models is proposed. The model is based on the generalized binomial distribution proposed by Luceño (1995) and Luceño and Ceballos (1995). The regression structure is modeled by using four different link functions and the dependence between the Bernoulli trials is modeled by using three different correlation functions. A data augmentation scheme is used in order to overcome the complexity of the mixture likelihood. A Bayesian method for inference is developed for the proposed model which relies on both the data augmentation scheme and the MCMC algorithms to obtain the posterior estimate for the parameters. Two types of Bayesian residuals and a local influence measure from a Bayesian perspective are proposed to check the underlying model assumptions, as well as to identify the presence of outliers and/or influential observations. Simulation studies are presented in order to illustrate the performance of the developed methodology. A real data set is analyzed by using the proposed models. Keywords: Binomial regression models; Generalized binomial distribution; Data augmentation scheme; Bayesian inference; Bayesian residuals; Local influence (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:8:p:2513-2525 DOI: 10.1016/j.csda.2012.02.004 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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