using the maximum likelihood method as well as the Delta method and Bootstrap methods. Data from an experiment is used to …

theta_n, its bootstrap approximation, and the Bayesian posterior for all agree asymptotically. It is shown that whenever g … is Lipschitz, though not necessarily differentiable, the posterior distribution of g(theta) and the bootstrap … distribution of g(theta_n) coincide asymptotically. One implication is that Bayesians can interpret bootstrap inference for g …

variance of the process. The latter is derived through bootstrap exercises using the models alone or pooled together. The …, the bootstrap exercises point to confidence bands close to 1 per cent around the estimated value. …

analysis output scores on nondiscretionary variables, both using Tobit and a single and double bootstrap procedure, we show …

intervals, using a bootstrap method for the Malmquist index. The study adjusts for the quality of the output by accounting for …

requirements that are not always obvious to the non-expert user. Bootstrap DEA is a significant development of the past decade …, which could be extended to test almost any hypothesis in bootstrap DEA. Moreover, it enhances the intuition behind bootstrap …