In order to check that a parametric model provides acceptable tail approximations, we present a test which compares the parametric estimate of an extreme upper quantile with its semiparametric estimate obtained by extreme value theory. To build this test, the sampling variations of these estimates are approximated through parametric bootstrap. Numerical Monte Carlo simulations explore the cover...

We provide a theoretical framework in which to study the accuracy of bootstrap P values, which may be based on a parametric or nonparametric bootstrap. In the parametric case, the accuracy of a bootstrap test will depend on the shape of what we call the critical value function. We show that, in many circumstances, the error in rejection probability of a bootstrap test will be one whole order of...

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle cl...

We discuss the application of the proportional hazard premium calculation principle in the parametric and non parametric framework. In the parametric approach, we propose a method to calculate the premium of a compound risk when the severity distribution is subexponential. In the non parametric approach, the use of the empirical distribution to calculate the premium using the proportional hazar...

Multiple imputation is a statistical method for analyzing data with missing values. Nonparametric Markov chain bootstrap methods can be used to generate multiple imputations of both scalar and multivariate outcome variables, under the assumption that the data are missing completely at random, and nonparametric inference can be obtained using multiple implementation bootstrap. The nonparametric ...

This paper introduces the confidence interval estimate for measuring the bullwhip effect, which has been observed across most industries. Calculating a confidence interval usually needs the assumption about the underlying distribution. Bootstrapping is a non-parametric, but computer intensive, estimation method. In this paper, a simulation study on the behavior of the 95% bootstrap confidence i...