This paper suggests two predictive likelihoods that can be applied in almost any parametric model setting. The first can sometimes be interpreted as an approximate predictive pivot (Barnard, 1986) while the second is often an approximation to a Bayesian predictive density with a flat prior. The issue of calibrating various predictive likelihoods in terms of long run predictive coverage is also discussed and a specific criterion by which these likelihoods can be compared is proposed
In this paper a second-order link between adjusted profile likelihoods and refinements of the estima...
Approximate Bayesian computation (ABC) methods, which are applicable when the like-lihood is difficu...
We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inf...
The notion of predictive likelihood stems from the fact that in the prediction problem there are two...
The notion of predictive likelihood stems from the fact that in the prediction problem there are two...
Abstract: For general regular parametric models, we compare predictive densities under the criterion...
We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable ...
We argue that prediction intervals based on predictive likelihood do not correct for curvature with ...
The paper makes a critical assessment of Aitchison's criterion of density estimation. It seems ...
This thesis consists in three essays on predictive distributions, in particular their combination, c...
Plug-in estimation and corresponding refinements involving penalisation have been considered in vari...
The notion of predictive likelihood relies on the likelihood principle for prediction and it corresp...
In this paper, a second-order link between adjusted profile likelihoods and refinements of the estim...
A model selection criterion based on Bayesian predictive densities is derived. Starting with an impr...
Let us consider a random vector Y distributed according to a statistical model depending on an unkno...
In this paper a second-order link between adjusted profile likelihoods and refinements of the estima...
Approximate Bayesian computation (ABC) methods, which are applicable when the like-lihood is difficu...
We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inf...
The notion of predictive likelihood stems from the fact that in the prediction problem there are two...
The notion of predictive likelihood stems from the fact that in the prediction problem there are two...
Abstract: For general regular parametric models, we compare predictive densities under the criterion...
We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable ...
We argue that prediction intervals based on predictive likelihood do not correct for curvature with ...
The paper makes a critical assessment of Aitchison's criterion of density estimation. It seems ...
This thesis consists in three essays on predictive distributions, in particular their combination, c...
Plug-in estimation and corresponding refinements involving penalisation have been considered in vari...
The notion of predictive likelihood relies on the likelihood principle for prediction and it corresp...
In this paper, a second-order link between adjusted profile likelihoods and refinements of the estim...
A model selection criterion based on Bayesian predictive densities is derived. Starting with an impr...
Let us consider a random vector Y distributed according to a statistical model depending on an unkno...
In this paper a second-order link between adjusted profile likelihoods and refinements of the estima...
Approximate Bayesian computation (ABC) methods, which are applicable when the like-lihood is difficu...
We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inf...