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...

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...

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...

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...

Unit root tests have been known for a long time to suffer from a variety of problems. Considering that simulation methods are obvious candidates for solving some of these problems, the aim of this paper is to assess the performance of bootstrap tests of the unit root hypothesis of the...

Under model correctness, highly accurate inference on a scalar interest parameter in the presence of a nuisance parameter can be achieved by several routes, among them considering the bootstrap distribution of the signed root likelihood ratio statistic. The context of model mis-specification is...