Modern Multivariate Statistical Techniques Regression, Classificatio
Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened
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Alan Julian Izenman
Modern Multivariate Statistical Techniques Regression, Classification, and Manifold Learning
Springer Texts in Statistics Series Editors: G. Casella S. Fienberg I. Olkin
Springer Texts in Statistics
For other titles published in this series, go to www.springer.com/series/417
Alan Julian Izenman
Modern Multivariate Statistical Techniques Regression, Classification, and Manifold Learning
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Alan J. Izenman Department of Statistics Temple University Speakman Hall Philadelphia, PA 19122 USA [email protected]
Editorial Board George Casella Department of Statistics University of Florida Gainesville, FL 32611-8545 USA
Stephen Fienberg Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213-3890 USA
Ingram Olkin Department of Statistics Stanford University Stanford, CA 94305 USA
ISSN 1431-875X ISBN 978-0-387-78188-4 ISBN 978-0-387-78189-1 (eBook) DOI 10.1007/978-0-387-78189-1 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2008928720 © Springer Science+Business Media New York 2008, Corrected at 2nd printing 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
This book is dedicated to the memory of my parents, Kitty and Larry,
and to my family, Betty-Ann and Kayla
Preface
Not so long
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