characterize their quantile breakdown point. For the moving block bootstrap and the subsampling, we find a very low quantile …We study the robustness of block resampling procedures for time series. We first derive a set of formulas to … breakdown point. A similar robustness problem arises in relation to data-driven methods for selecting the block size in …

to the assumed logistic regression model, after the set of all true confounders are included. This is done via an … logistic regression. Our results show that in general, adding non-confounders to the regression model decreases the mean …

different bootstrap tests. In the context of static linear regression modelstwo of these are shown to have serious size and … implementations is shown tobreak down in dynamic regression models. Then the procedure based on the test statistic approachperforms …

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 …

In an attempt to free bootstrap theory from the shackles of asymptotic considerations, this paper studies the … possibility of justifying, or validating, the bootstrap, not by letting the sample size tend to infinity, but by considering the … sequence of bootstrap P values obtained by iterating the bootstrap. The main idea of the paper is that, if this sequence …

bootstrap algorithm to implement the proposed test and show its asymptotic validity. The proposed test procedure can apply to …

In the presence of conditional heteroskedasticity, inference about the coefficients in a linear regression model these … shown that a bootstrap approximation to the sampling distribution of the weighted least squares estimate is valid, which …-sample properties of this new estimator as well as the improvements in performance realized by bootstrap confidence intervals. …