In less than two decades, the bootstrap has grown from an obscure object of theoretical study to a widely used resampling method with borad applications in numerous real-world situations. Bootstrap Methods: A Practitioner's Guide provides an introduction to the bootstrap for readers who have professional interst 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 dicriminant 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 consitency 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 explantations of when and why bootstrap is not effective.
