Count data in practice rarely conform to the equidispersion assumption that the standard Poisson regression model imposes. When counts are overdispersed or underdispersed, the generalized Poisson regression model (GPRM) steps in as a more accommodating alternative, extending the classical framework to handle such varia...
This study presents a fast and consistent software package called BACE (i.e., Bayesian averaging of classical estimates) that offers a model-building strategy for various bounded dependent variable models, including – logit and probit models, ordered logit and pro-bit models, multinomial logistic regression, Poisson re...
This paper develops a Bayesian estimation framework for the logarithmic Fréchet regression model with right-censored lifetime data. We establish four theoretical results: model identifiability via the Gumbel characteristic function, posterior propriety under normalhalf-normal priors, validity of the Bayesian predictive...
The log Gaussian Cox Process (LGCP) is one of the most widely used models for the analysis of spatial point patterns. Although Bayesian methods and software for fitting LGCPs are now well established, practical tools for model criticism, predictive assessment, and model comparison remain comparatively underdeveloped. T...
In this work, we consider the Frank copula, belonging to the Archimedean family and enjoying several interesting properties, and suggest its use as a bivariate and multivariate latent variable for modeling correlated ordinal data, exploring the relationship between its parameter, which models the dependence among the...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
