Over-Dispersion

Over-dispersion depicts the phenomenon that the spread in the data is wider than compatible with Gaussian modeling. The phenomenon may occur both with continuous and discrete data, although more commonly with discrete data.

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chine Learning in Medicine Part Three

Machine Learning in Medicine

Ton J. Cleophas • Aeilko H. Zwinderman

Machine Learning in Medicine Part Three

Ton J. Cleophas Department Medicine Albert Schweitzer Hospital Sliedrecht, The Netherlands

Aeilko H. Zwinderman Department Biostatistics and Epidemiology Academic Medical Center Amsterdam, The Netherlands

Additional material to this book can be downloaded from extras.springer.com ISBN 978-94-007-7868-9 ISBN 978-94-007-7869-6 (eBook) DOI 10.1007/978-94-007-7869-6 Springer Dordrecht Heidelberg New York London Library of Congress Control Number: 2013955741 © Springer Science+Business Media Dordrecht 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)

Machine Learning in Medicine Part Three

by TON J. CLEOPHAS, MD, PhD, Professor, Past-President American College of Angiology, Co-Chair Module Statistics Applied to Clinical Trials, European Interuniversity College of Pharmaceutical Medicine, Lyon, France, Department Medicine, Albert Schweitzer Hospital, Dordrecht, Netherlands AEILKO H. ZWINDERMAN, MathD, PhD, Professor, President International Society of Biostatistics, Co-Chair Module Statistics Applied to Clinical Trials, European Interuniversity College of Pharmaceutical Medicine, Lyon, France, Department Biostatisti

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