Add to ORKG1. Introduction The Robustness, of a statistical procedure, is commonly defined as the stability with respect to small changes in the assumptions. This notion has immediate intuitive appeal and it has even been equated to the Holy Grail of Statistics (see [5])
Imprecise probability methods are often claimed to be robust, or more robust than conventional metho...
This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The ob...
The field of mathematical statistics called robust statistics appeared due to the pioneer works of T...
Robustness has always been an important element of the foundation of Statistics. However, it has onl...
This paper presents a new asymptotic approach to study the robustness of Bayesian inference to chang...
We develop a framework for quantifying the sensitivity of the distribution of pos-terior distributio...
International audienceFor a given statistical method, good properties are usually obtained under str...
In lieu of an abstract, here is the entry\u27s first paragraph: Robust statistics are procedures tha...
With the advent of high-performance computing, Bayesian methods are becoming increasingly popular to...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
In the first part of the paper, we trace the development of robust statistics through its main contr...
Robust statistics, as a concept, probably dates back to the prehistory of statistics. It has, howeve...
This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two diffe...
With the advent of high-performance computing, Bayesian methods are becoming increasingly popular to...
The topic of robustness is experiencing a resurgence of interest in the statistical and machine lear...
Imprecise probability methods are often claimed to be robust, or more robust than conventional metho...
This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The ob...
The field of mathematical statistics called robust statistics appeared due to the pioneer works of T...
Robustness has always been an important element of the foundation of Statistics. However, it has onl...
This paper presents a new asymptotic approach to study the robustness of Bayesian inference to chang...
We develop a framework for quantifying the sensitivity of the distribution of pos-terior distributio...
International audienceFor a given statistical method, good properties are usually obtained under str...
In lieu of an abstract, here is the entry\u27s first paragraph: Robust statistics are procedures tha...
With the advent of high-performance computing, Bayesian methods are becoming increasingly popular to...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
In the first part of the paper, we trace the development of robust statistics through its main contr...
Robust statistics, as a concept, probably dates back to the prehistory of statistics. It has, howeve...
This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two diffe...
With the advent of high-performance computing, Bayesian methods are becoming increasingly popular to...
The topic of robustness is experiencing a resurgence of interest in the statistical and machine lear...
Imprecise probability methods are often claimed to be robust, or more robust than conventional metho...
This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The ob...
The field of mathematical statistics called robust statistics appeared due to the pioneer works of T...