Change detection in hydrological records—a review of the methodology / Revue méthodologique de la détection de changements dans les chroniques hydrologiques <s> Introducing resampling methods <s> Introduction The Accuracy of a Sample Mean Random Samples and Probabilities The Empirical Distribution Function and the Plug-In Principle Standard Errors and Estimated Standard Errors The Bootstrap Estimate of Standard Error Bootstrap Standard Errors: Some Examples More Complicated Data Structures Regression Models Estimates of Bias The Jackknife Confidence Intervals Based on Bootstrap "Tables" Confidence Intervals Based on Bootstrap Percentiles Better Bootstrap Confidence Intervals Permutation Tests Hypothesis Testing with the Bootstrap Cross-Validation and Other Estimates of Prediction Error Adaptive Estimation and Calibration Assessing the Error in Bootstrap Estimates A Geometrical Representation for the Bootstrap and Jackknife An Overview of Nonparametric and Parametric Inference Further Topics in Bootstrap Confidence Intervals Efficient Bootstrap Computations Approximate Likelihoods Bootstrap Bioequivalence Discussion and Further Topics Appendix: Software for Bootstrap Computations References <s> BIB001 </s> Change detection in hydrological records—a review of the methodology / Revue méthodologique de la détection de changements dans les chroniques hydrologiques <s> Introducing resampling methods <s> This book provides a step-by-step manual on the application of permutation tests in biology, medicine, science, and engineering. Its intuitive and informal style will ideally suit it as a text for students and researchers coming to these methods for the first time. In particular, it shows how the problems of missing and censored data, nonresponders, after-the-fact covariates, and outliers may be handled. <s> BIB002
Resampling methods (permutation or bootstrap) are robust techniques for estimating the significance level of a test statistic. A useful practical text on resampling and permutation is provided by BIB002 , while BIB001 and  describe bootstrapping methods. The advantages of resampling are that it is a flexible method that can be adapted to a wide range of types of data, including autocorrelated or seasonal data. Resampling is also relatively powerful, e.g. for large samples, permutation tests can be shown to be as powerful as the most powerful parametric tests  . Resampling methods are very useful for testing hydrological data because they require relatively few assumptions to be made about the data, yet they are also quite powerful tests.