Numerical Analysis for Statisticians
Every advance in computer architecture and software tempts statisticians to tackle numerically harder problems. To do so intelligently requires a good working knowledge of numerical analysis. This book equips students to craft their own software and to un
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Kenneth Lange
Numerical Analysis for Statisticians Second Edition
Kenneth Lange Departments of Biomathematics, Human Genetics, and Statistics David Geffen School of Medicine University of California, Los Angeles Le Conte Ave. 10833 Los Angeles, CA 90095-1766 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
ISSN 1431-8784 ISBN 978-1-4419-5944-7 e-ISBN 978-1-4419-5945-4 DOI 10.1007/978-1-4419-5945-4 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010927594 © Springer Science+Business Media, LLC 2010 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 to the Second Edition More than a decade has passed since the publication of the first edition of Numerical Analysis for Statisticians. During the interim, statistics rapidly evolved as a discipline. In particular, Markov chain Monte Carlo methods, data mining, and software resources all substantially improved. My own understanding of several subjects also matured. I accordingly set out to write a better book, trying to update topics and correct the errors and omissions of the first edition. The result is certainly a longer book. Whether it is a better book is probably best left to the judgment of readers. One thing I learned from Springer’s stable of reviewers is that there is no universally agreed-on environment for statistical computing. My own preference for coding algorithms in Fortran was quickly dismissed as unworkable. The suggestion of C or C++ might have garnered wider support, but no doubt Java devotees would have objected. In fact, most statisticians prefer higher-level environments such as SAS, R, or Matlab. Each of these environments has its advantages, but none is dominant. For computationally intensive tasks, interpreted languages are at a disadvantage, so compiled languages such as C, Fortran, and Java will survive. A more interesting questio
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