a widely used resampling method with broad applications in numerous real-world situations. Bootstrap Methods: A Practitioner's Guide provides an introduction to the bootstrap for readers who have professional interest in these methods but do not have a background in advanced mathematics. It offers reliable, authoritative coverage of the bootstrap's considerable advantages as well as its drawbacks. This book updates classic texts in the field by presenting results on improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems. To alert readers to the limitations of the method, it exhibits counterexamples to the consistency of bootstrap methods. This book also makes connections between more traditional resampling methods and bootstrap. Outstanding special features of Bootstrap Methods include: The most extensive and detailed bootstrap bibliography available, including more than 1,600 references Discussions enlivened with stimulating topics such as data mining Historical notes at the end of each chapter Examples and explanations of when and why bootstrap is not effective Bootstrap Methods is a serious, useful, and unparalleled practical guide for professionals in engineering, the sciences, clinical medicine, and applied statistics.

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