We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and...

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and...

Multiple time series data may exhibit clustering over time and the clustering effect may change across different series. This paper is motivated by the Bayesian non-parametric modelling of the dependence between clustering effects in multiple time series analysis. We follow a Dirichlet process...

Multiple time series data may exhibit clustering over time and the clustering effect may change across different series. This paper is motivated by the Bayesian non–parametric modelling of the dependence between clustering effects in multiple time series analysis. We follow a Dirichlet process...

We analyze a class of smoothing transformations on probability measures in multiple space dimensions. Applying a synthesis of probabilistic methods and Fourier analysis, we prove existence and uniqueness of a fixed point inside the class of probability measures of finite second moment,...

The main object of Bayesian statistical inference is the determination of posterior distributions. Sometimes these laws are given for quantities devoid of empirical value. This serious drawback vanishes when one confines oneself to considering a finite horizon framework. However, assuming...

This article introduces estimators defined as minimizers of Kantorovich distances between statistical models and empirical distributions. Existence, measurability and consistency of these estimators are studied. A few significant examples illustrate the applicability of the theoretical results...

Consistency of minimum divergence estimators, based on grouped data, is studied under conditions which, to our knowledge, are weaker than the ones considered in the existing literature. Comments on the hypotheses and the interpretation of the main results are made, and an illustrative example is...

In this paper we study the Metropolis algorithm in connection with two mean–field spin systems, the so called mean–field Ising model and the Blume–Emery–Griffiths model. In both this examples the naive choice of proposal chain gives rise, for some parameters, to a slowly mixing...