A general bootstrap algorithm for hypothesis testing

The bootstrap is a intensive computer-based method originally mainly devoted to estimate the standard deviations, confidence intervals and bias of the studied statistic. This technique is useful in a wide variety of statistical procedures, however, its use for hypothesis testing, when the data structure is complex, is not straightforward and each case must be particularly treated. A general bootstrap method for hypothesis testing is studied. The considered method preserves the data structure of each group independently and the null hypothesis is only used in order to compute the bootstrap statistic values (not at the resampling, as usual). The asymptotic distribution is developed and several case studies are discussed. (C) 2011 Elsevier B.V. All rights reserved.