Computational Statistics
Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power
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James E.Gentle
Computational Statistics
Statistics and Computing Series Editors: J. Chambers D. Hand W. Härdle
For other titles published in this series, go to http://www.springer.com/series/3022
James E. Gentle
Computational Statistics
J.E. Gentle Department of Computational & Data Sciences George Mason University 4400, University Drive Fairfax, VA 22030-4444 USA [email protected]
Series Editors: J. Chambers Department of Statistics Sequoia Hall 390 Serra Mall Stanford University Stanford, CA 94305-4065
D. Hand Department of Mathematics Imperial College London, South Kensington Campus London SW7 2AZ United Kingdom
W. Härdle Institut für Statistik und Ökonometrie Humboldt-Universität zu Berlin Spandauer Str. 1 D-10178 Berlin Germany
e-ISBN 978-0-387-98144-4 ISBN 978-0-387-98143-7 DOI 10.1007/978-0-387-98144-4 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2009929633 © Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.
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Preface
This book began as a revision of Elements of Computational Statistics, published by Springer in 2002. That book covered computationally-intensive statistical methods from the perspective of statistical applications, rather than from the standpoint of statistical computing. Most of the students in my courses in computational statistics were in a program that required multiple graduate courses in numerical analysis, and so in my course in computational statistics, I rarely covered topics in numerical linear algebra or numerical optimization, for example. Over the years, however, I included more discussion of numerical analysis in my computational statistics courses. Also over the years I have taught numerical methods courses with no or very little statistical content. I have also accumulated a number of corrections and small additions to the elements of computational statistics. The present book includes most of the topics from Elements and also incorporates this additional material. The emphasis is still on computationallyintensive statistical methods, but there is a substantial portion on the numerical methods supporting the statistical applications. I have attempted to provide a broad coverage of the field of computational statisti
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