We present a variance stabilizing transformation for inference about a scalar parameter that is estimated by a function of a multivariate "M"-estimator. The transformation proposed is automatic, computationally simple and can be applied quite generally. Though it is based on an intuitive notion...

Objective Bayes methodology is considered for conditional frequentist inference about a canonical parameter in a multi-parameter exponential family. A condition is derived under which posterior Bayes quantiles match the conditional frequentist coverage to a higher-order approximation in terms of...

Bayesian properties of the signed root likelihood ratio statistic are analysed. Conditions for first-order probability matching are derived by the examination of the Bayesian posterior and frequentist means of this statistic. Second-order matching conditions are shown to arise from matching of...

Asymptotic approximations of marginal densities and tail probabilities for smooth functions of a continuous random vector, developed by Tierney, Kass and Kadane (1989) and DiCiccio and Martin (1991), respectively, in general fail to be invariant under transformations of the underlying random...

Bootstrap methods are attractive empirical procedures for assessment of errors in problems of statistical estimation, and allow highly accurate inference in a vast range of parametric problems. Conventional parametric bootstrapping involves sampling from a fitted parametric model, obtained by...