The aim of this book is to present a readable introduction to the theory of bootstrap methods which is suitable for graduate students and research workers in statistics. In particular, the author discusses the application of bootstrap methods to linear regression, non-parametric regression and density estimation. The author's perspective is that the Edgeworth expansion sheds important light on the performance of bootstrap methods and that, conversely, bootstrap methods motivate a renewed interest in the study of the ...
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The aim of this book is to present a readable introduction to the theory of bootstrap methods which is suitable for graduate students and research workers in statistics. In particular, the author discusses the application of bootstrap methods to linear regression, non-parametric regression and density estimation. The author's perspective is that the Edgeworth expansion sheds important light on the performance of bootstrap methods and that, conversely, bootstrap methods motivate a renewed interest in the study of the Edgeworth expansion. Consequently, the book is structured to first present chapters which introduce the basic concepts of bootstrap methods and the Edgeworth expansion. Subsequent chapters then explore the interaction between the two subjects and their application to statistical techniques. Generally, technical details are deferred to the last chapter so as to enable a reader with a relatively small exposure to theoretical statistics to enjoy this account of a rapidly growing branch of statistical research.
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