Assessing the losses caused by an industrial intervention: A hierarchical Bayesian approach

A nonparametric multiplicative hazard model is proposed, and applied to the estimation of losses caused by a strike at a Quebec aluminium smelter in 1967. The Bayesian approach is adopted, and Gibbs sampling for the numerical computations. We obtain a posterior distribution of the hazard rate and the corresponding predictive distribution of the projected losses. The main merit of the approach proposed is that the uncertainties concerning the actual losses, both in terms of the total number of operating days lost and the excess number of damaged aluminium reduction cells, can be quantified with probabilities. The compensation of the losses is discussed, and the results are compared with some point estimates from earlier analyses of the same data.