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Editorial Board: R. Gill Utrecht B. Ripley Oxford S. Ross Berkeley M. Stein Chicago D. Williams Bath. This series of high quality upper-division textbooks and expository mono graphs covers all areas of stochastic applicable mathematics.

The topics range from pure and applied statistics to probability theory, operations re search, mathematical programming, and optimzation. The books contain clear presentations of new developments in the field and also of the state of the art in classical methods. While emphasizing rigorous treatment of the oretical methods, the books contain important applications and discussions of new techniques made possible be advances in computational methods.

Bootstrap methods and their application. This book is in copyright. Subject to statutory exception and to the provisions o f relevant collective licensing agreements, no reproduction o f any part may take place without the written permission o f Cambridge University Press.

D avison, D. Includes bibliographical references and index. ISB N 0 2 hb. ISBN 0 4 pb 1. Bootstrap Statistics I. Hinkley, D. D38 Tests 4. Confidence Intervals 5. Linear Regression 6. Appendix A. The publication in of Bradley Efrons first article on bootstrap methods was a major event in Statistics, at once synthesizing some of the earlier resampling ideas and establishing a new framework for simulation-based statistical analysis.

The idea of replacing complicated and often inaccurate approximations to biases, variances, and other measures of uncertainty by com puter simulations caught the imagination of both theoretical researchers and users of statistical methods. Theoreticians sharpened their pencils and set about establishing mathematical conditions under which the idea could work. Once they had overcome their initial skepticism, applied workers sat down at their terminals and began to amass empirical evidence that the bootstrap often did work better than traditional methods.

The early trickle of papers quickly became a torrent, with new additions to the literature appearing every month, and it was hard to see when would be a good moment to try to chart the waters. We decided to try to write a balanced account o f resampling methods, to include basic aspects of the theory which underpinned the methods, and to show as many applications as we could in order to illustrate the full potential of the methods warts and all. We quickly realized that in order for us and others to understand and use the bootstrap, we would need suitable software, and producing it led us further towards a practically oriented treatment.

Our view was cemented by two further developments: the appearance o f two excellent books, one by Peter Hall on the asymptotic theory and the other on basic methods by Bradley Efron and Robert Tibshirani; and the chance to give further courses that included practicals. O ur experience has been that hands-on computing is essential in coming to grips with resampling ideas, so we have included practicals in this book, as well as more theoretical problems.

As the book expanded, we realized that a fully comprehensive treatm ent was beyond us, and that certain topics could be given only a cursory treatm ent because too little is known about them. So it is that the reader will find only brief accounts o f bootstrap methods for hierarchical data, missing data problems, model selection, robust estimation, nonparam etric regression, and complex data. But we do try to point the more ambitious reader in the right direction.

No project of this size is produced in a vacuum. The majority of work on the book was completed while we were at the University of Oxford, and we are very grateful to colleagues and students there, who have helped shape our work in various ways. The experience of trying to teach these methods in Oxford and elsewhere at the Universite de Toulouse I, Universite de Neuchatel, Universita degli Studi di Padova, Queensland University of Technology, Universidade de Sao Paulo, and University of Umea has been vital, and we are grateful to participants in these courses for prompting us to think more deeply about the.

Readers will be grateful to these people also, for unwittingly debugging some of the problems and practicals. While writing this book we have asked many people for access to data, copies of their programs, papers or reprints; some have then been rewarded by our bombarding them with questions, to which the answers have invariably been courteous and informative. We cannot name all those who have helped in this way, but D. Brillinger, P. Hall, M.

Jones, B. Ripley, H. Sternberg and G. Young have been especially generous. Hutchinson and B. Ripley have helped considerably with computing matters. We are grateful to the mostly anonymous reviewers who commented on an early draft of the book, and to R.

G atto and G. Young, who later read various parts in detail. A t Cambridge University Press, A. W oollatt and D. Tranah have helped greatly in producing the final version, and their patience has been commendable. We are particularly indebted to two people. Ventura read large portions o f the book, and helped with various aspects of the com putation. Canty has turned our version o f the bootstrap library functions into reliable working code, checked the book for mistakes, and has made numerous suggestions that have improved it enormously.

Both of them have contributed greatly though o f course we take responsibility for any errors that remain in the book. We hope that readers will tell us about them, and we will do our best to correct any future versions of the book; see its WWW page, at U R L. The book could not have been completed without grants from the U K Engineer ing and Physical Sciences Research Council, which in addition to providing funding for equipment and research assistantships, supported the work o f A.

Davison through the award o f an Advanced Research Fellowship. The projects of many authors have flourished in these amiable establishments. Finally, we thank our families, friends and colleagues for their patience while this project absorbed our time and energy.

Particular thanks are due to Claire Cullen Davison for keeping the Davison family going during the writing of this book. Davison and D. The explicit recognition o f uncertainty is central to the statistical sciences. N o tions such as prior inform ation, probability models, likelihood, stan d ard errors an d confidence limits are all intended to form alize uncertainty and thereby m ake allow ance for it. In sim ple situations, the uncertainty o f an estim ate may be gauged by analytical calculation based on an assum ed probability m odel for the available data.

But in m ore com plicated problem s this approach can be tedious an d difficult, and its results are potentially m isleading if inappropriate assum ptions or sim plifications have been made. F or illustration, consider Table 1. R eports are cross-classified by diagnosis period an d length o f reporting delay, in three-m onth intervals.

A blank in the table corresponds to an unknow n as yet unreported entry. The problem was to predict the states o f the epidem ic in and , which depend heavily on the values missing at the b o tto m right o f the table. T he d a ta su p p o rt the assum ption th at the reporting delay does n o t depend on the diagnosis period.

If all the cells o f the table are regarded as independent, then the to tal nu m b er o f u n reported diagnoses in period j has a Poisson distribution w ith m ean.

The eventual total o f as yet u n rep o rted diagnoses from period j can be estim ated by replacing a j and Pk by estim ates derived from the incom plete table, and thence we obtain the predicted to tal for period j. Such predictions are shown by the solid line in. Table 1. A t indicates 1 31 80 16 9 3 2 8 6 a reporting delay less 2 26 99 27 9 8 11 3 3 than one month. Figure 1. How good are these predictions? It would be tedious b u t possible to p u t pen to p ap er and estim ate the prediction uncertainty th ro u g h calculations based on the Poisson model.

But in fact the d a ta are m uch m ore variable th an th a t m odel would suggest, and by failing to take this into account we w ould believe th at the predictions are m ore accurate th a n they really are.

Furtherm ore, a b etter approach would be to use a sem iparam etric m odel to sm ooth out the evident variability o f the increase in diagnoses from q u arter to q u arter; the corresponding prediction is the dotted line in Figure 1. A nalytical calculations for this m odel would be very unpleasant, and a m ore flexible line o f attack is needed.

W hile m ore th an one approach is possible, the one th a t we shall develop based on com puter sim ulation is b o th flexible and straightforw ard. Purpose of the Book O ur central goal is to describe how the com puter can be harnessed to obtain reliable stan d ard errors, confidence intervals, and o th er m easures o f uncertainty for a wide range o f problem s.

Because this approach involves repeating the original d a ta analysis procedure w ith m any replicate sets o f data, these are som etim es called computer-intensive methods. A n o th er nam e for them is bootstrap methods, because to use the d a ta to generate m ore d a ta seems analogous to a trick used by the fictional B aron M unchausen, who when he found him self a t the b o tto m o f a lake got out by pulling him self up by his b ootstraps.

In the sim plest nonparam etric problem s we do literally sample from the data, and a com m on initial reaction is th a t this is a fraud. In fact it is not. It turns out th a t a wide range o f statistical problem s can be tackled this way, liberating the investigator from the need to oversimplify complex problem s.

T he ap proach can also be applied in simple problem s, to check the adequacy o f stan d ard m easures o f uncertainty, to relax assum ptions, and to give quick approxim ate solutions.

A n exam ple o f this is random sam pling to estim ate the p erm u tatio n distribution o f a nonparam etric test statistic. It is o f course true th a t in m any applications we can be fairly confident in a p articu lar p aram etric m odel and the stan d ard analysis based on th a t model.

Even so, it can still be helpful to see w hat can be inferred w ithout particular p aram etric m odel assum ptions. This is in the spirit o f robustness o f validity o f the statistical analysis perform ed.

Bootstrap Methods

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Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Bootstrap Methods and Applications Abstract: Given the wealth of literature on the topic supported by solutions to practical problems, we would expect the bootstrap to be an off-the-shelf tool for signal processing problems as are maximum likelihood and least-squares methods. This is not the case, and we wonder why a signal processing practitioner would not resort to the bootstrap for inferential problems.

Editorial Board: R. Gill Utrecht B. Ripley Oxford S. Ross Berkeley M. Stein Chicago D. Williams Bath. This series of high quality upper-division textbooks and expository mono graphs covers all areas of stochastic applicable mathematics.

Bootstrap methods and their application

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BOOTSTRAP METHOD AND THEIR APPLICATION USING R-PROGRAMMING

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