, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values … conditions are not fully met, bootstrap may lead to unstable critical values that change significantly with the alternative …, whereas when all conditions are met, bootstrap critical values are very stable, approximate much better the finite sample …

, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values … conditions are not fully met, bootstrap may lead to unstable critical values that change significantly with the alternative …, whereas when all conditions are met, bootstrap critical values are very stable, approximate much better the finite sample …

different bootstrap tests. In the context of static linear regression modelstwo of these are shown to have serious size and …

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

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 …

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 …

different bootstrap tests. In the context of static linear regression modelstwo of these are shown to have serious size and …