This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a...

The quantitative study of marked individuals relies mainly on the use of meaningful biological models. Classical inference is then conducted based on the model likelihood, parameterized by parameters such as survival, recovery, transition and recapture probabilities. In classical statistics, we...

There are few analytic tools available to formally integrate information coming from population surveys and demographic studies. The Kalman filter is a procedure that facilitates such integration. Based on a state-space model, we can obtain a likelihood function for the survey data using a...

The past decade has seen a remarkable development in the area of Bayesian nonparametric inference from both theoretical and applied perspectives. As for the latter, the celebrated Dirichlet process has been successfully exploited within Bayesian mixture models, leading to many interesting...

This paper proposes a predictive approach to Bayesian model selection based on independent and identically distributed observations. In particular, we generalise the criterion of San Martini and Spezzaferri (J. Roy. Statist. Soc. B 46 (1984) 296–303) to take into account more realistic views...

We deal with smoothed estimators for conditional probability functions of discrete-valued time series (Y-1) under two different settings. When the conditional distribution of Y-1 given its lagged values falls in a parametric family and depends on exogenous random variables, a smoothed maximum...

In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, 'diffuse' priors on model-specific parameters can lead to quite unexpected...