A Hierarchical Bayes Analysis and Comparison of PH Weibull and PH Exponential Models for One-Shot Device Testing Experiment

The one-shot devices are highly reliable and, therefore, accelerated life tests are often employed to perform the experiments on such devices. Obviously, in the process, some covariates are introduced. This paper considers the proportional hazards model to observe the effect of covariates on the failure rates under the assumption of two commonly used models, namely the exponential and the Weibull for the lifetimes. The Bayes implementation is proposed using the hybridization of Gibbs and Metropolis algorithms that routinely extend to missing data situations as well. The entertained models are compared using the Bayesian and deviance information criteria and the expected posterior predictive loss criterion. Finally, the results based on two real data examples are given as an illustration.

Core Inflation: Robust Common Trend Model Forecasting

Monetary authorities need a measure of the future inflation trend to keep inflation on target. Many alternative core inflation measures appear in the recent literature intending to avoid the deficiencies of the usual headline inflation index as a predictor. This price index is defined as some weighted average of the individual price change for a list of goods and services. Its use as a future inflation indicator is criticized in the literature because the products are heterogeneous in respect to the variability and some of the prices involved have relevant seasonal movements. A multivariate model simultaneously including the seasonal effects of each component of the price index and a common trend - core inflation - will be developed in this paper. The model is phrased as a dynamic model and a robust sequential filter is introduced. The posterior and predictive distributions of the quantities of interest are evaluated via stochastic simulation techniques, MCMC - Markov chain Monte Carlo. Different models are compared using the minimum posterior predictive loss approach and many graphical illustrations are presented.