This article examines the sensitivity of inferences to perturbations at various stages of a
hierarchically specified prior. For the most part, a local method of assessing sensitivity is …

The purpose of this paper is to develop a Bayesian approach for the Weibull-Negative-
Binomial regression model with cure rate under latent failure causes and presence of …

Among all the technologies of data mining, predictive classification has a wide range of
application. People do some prediction by building up classification models and hope to …

One of the most prominent goals of Bayesian robustness is to study the sensitivity of final
answers to the various inputs of a statistical analysis. Also, since the use of extra …

We propose to use χ2 and Harmonic divergences as global measures of accuracy of an
approximation π ̂ to a posterior density of interest π. We prove some inequalities which …

This paper develops a Bayesian approach to obtain the joint estimates of unknown
parameters, nonparametric functions and random effects in generalized partially linear …

Consider two models M1 and M2 proposed as models for the same data. Assume that the
conclusions from both models are posteriors and of some inferential target θ given the data …

In this work we approach the problem of model comparison between skew families. For the
univariate skew model, we measure the sensitivity of the skewness parameter using the L1 …

Although Bayesian inference is a popular paradigm among a large segment of scientists,
including statisticians, most applications consider objective priors and need critical …

The purpose of this paper is to develop a Bayesian analysis for the zero-inflated hyper-
Poisson model. Markov chain Monte Carlo methods are used to develop a Bayesian …