Classical confidence intervals for the ratio of variance components can degenerate to a single point with positive probability, leaving no allowance for error. An alternative method is considered which avoids this problem, but maintains the frequentist coverage probability. The alternative intervals are compared to Bayesian credible intervals, derived from a non-informative prior; and the Bayes...

In this paper, we adopt a Bayesian point of view for predicting real stochastic processes. We give two equivalent definition of a Bayesian predictor and study some properties: admissibility, prediction sufficiency, unbiasedness, comparison with efficient predictors. Prediction of Poisson process and prediction of Ornstein-Uhlenbeck process in the continuous and sampled situations are considered...

This article presents a Bayesian approach to forecast mortality rates. This approach formalizes the Lee-Carter method as a statistical model accounting for all sources of variability. Markov chain Monte Carlo methods are used to fit the model and to sample from the posterior predictive distribution. This paper also shows how multiple imputations can be readily incorporated into the model to han...

We extend the results of De Luca et al. (2021) to inference for linear regression models based on weighted-average least squares (WALS), a frequentist model averaging approach with Bayesian flavor. We concentrate about single focus parameter, interpreted as causal effect policy or intervention, in presence potentially large number auxiliary parameters representing nuisance component model. In ...