Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of...

En este trabajo se propone un modelo jerárquico Gamma para el estudio bayesiano de tiempos de desempleo con covariables utilizando técnicas de análisis de supervivencia. Se trata de un modelo de efectos aleatorios o modelo de poblaciones debido a su estructura jerárquica, que permite la...

This paper deals with empirical processes of the type Cn(B) = n^(1/2) {µn(B) - P(Xn+1 in B

When the future evolution of demographic processes is described in a stochastic setting, the challenge is to communicate the meaning of forecast uncertainty in an understandable way, to decision makers and public at large. For the purpose of risk communication, a formal setting is developed, in...

This paper deals with empirical processes of the type Cn(B) = n^(1/2) {µn(B) - P(Xn+1 in B | X1, . . . ,Xn)} , where (Xn) is a sequence of random variables and µn = (1/n)SUM(i=1,..,n) d(Xi) the empirical measure. Conditions for supB|Cn(B)| to converge stably (in particular, in distribution)...