Add to ORKGAn upper bound for the Kolmogorov distance between the posterior distributions in terms of that between the prior distributions is given. For some likelihood functions the inequality is sharp. Applications to assessing Bayes robustness are presented
This paper develops a methodology for robust Bayesian inference through the use of disparities. Met-...
Comparing continuous distributions with respect to a reference distribution is a problem which occur...
AbstractWe provide the rate of convergence of the Bayes action derived from non smooth loss function...
In Chapter 2, the robustness of Bayes analysis with reference to conjugate prior classes is discusse...
This paper introduces a new family of local density separations for assessing robustness of finite-di...
This paper presents a new asymptotic approach to study the robustness of Bayesian inference to chang...
This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two diffe...
The local sensitivity analysis is recognized for its computational simplicity, and potential use in ...
Under a new family of separations the distance between two poste-rior densities is the same as the d...
Abstract The paper describes one possible robustification process on Bayes esti-mators and studies h...
We develop a framework for quantifying the sensitivity of the distribution of pos-terior distributio...
Robustness of classical and Bayesian inference is considered when exact model assumptions are violat...
The first part of the thesis concerns itself with Bayesian nonparametrics. We consider the problem o...
Robustness has always been an important element of the foundation of Statistics. However, it has onl...
Here a new class of local separation measures over prior densities is studied and their usefulness ...
This paper develops a methodology for robust Bayesian inference through the use of disparities. Met-...
Comparing continuous distributions with respect to a reference distribution is a problem which occur...
AbstractWe provide the rate of convergence of the Bayes action derived from non smooth loss function...
In Chapter 2, the robustness of Bayes analysis with reference to conjugate prior classes is discusse...
This paper introduces a new family of local density separations for assessing robustness of finite-di...
This paper presents a new asymptotic approach to study the robustness of Bayesian inference to chang...
This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two diffe...
The local sensitivity analysis is recognized for its computational simplicity, and potential use in ...
Under a new family of separations the distance between two poste-rior densities is the same as the d...
Abstract The paper describes one possible robustification process on Bayes esti-mators and studies h...
We develop a framework for quantifying the sensitivity of the distribution of pos-terior distributio...
Robustness of classical and Bayesian inference is considered when exact model assumptions are violat...
The first part of the thesis concerns itself with Bayesian nonparametrics. We consider the problem o...
Robustness has always been an important element of the foundation of Statistics. However, it has onl...
Here a new class of local separation measures over prior densities is studied and their usefulness ...
This paper develops a methodology for robust Bayesian inference through the use of disparities. Met-...
Comparing continuous distributions with respect to a reference distribution is a problem which occur...
AbstractWe provide the rate of convergence of the Bayes action derived from non smooth loss function...