Bootstrap Methods: A Practitioner's Guide
A comprehensive, practical treatment for professionals In less than two decades, the bootstrap has grown from an obscure object of theoretical study to 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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What is Bootstrapping?
Conﬁdence Sets and Hypothesis Testing
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632 estimator applications apply the bootstrap Assoc asymptotic autoregressive Bayesian bootstrap Beran bias Billard Biometrika bootstrap approach bootstrap conﬁdence intervals bootstrap distribution bootstrap estimate bootstrap iteration bootstrap methods bootstrap sample bootstrap statistical Chernick classiﬁcation coefﬁcient Comput covariance cross-validation Davison and Hinkley deﬁned delta method discussion Edgeworth expansions editors Efron and Tibshirani empirical distribution Encyclopedia of Statistical error rate estimation ﬁnite ﬁrst function Gaussian hypothesis testing inference inﬂuence J. R. Statist jackknife jackknife estimate Kotz LePage Limits of Bootstrap linear models M-estimates Math McLachlan Monte Carlo approximation Monte Carlo methods Multivariate Murthy nonlinear nonlinear regression nonparametric bootstrap nonparametric regression observations p-value parameters percentile method prediction intervals probability procedure process capability indices quantile random regression models resampling resampling methods residuals robust sample mean Section signiﬁcance simulation smoothed speciﬁcation Springer-Verlag standard errors Statistical Sciences Stochastic techniques theoretical Theory Methods variables vector Wiley York