Dynamic Data Analysis Modeling Data with Differential Equations
This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analy
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James Ramsay Giles Hooker
Dynamic Data Analysis Modeling Data with Differential Equations
Springer Series in Statistics Series Editors Peter Bickel, Berkeley, CA, USA Peter Diggle, Lancaster, UK Stephen E. Fienberg, Pittsburgh, PA, USA Ursula Gather, Dortmund, Germany Scott Zeger, Baltimore, MD, USA
More information about this series at http://www.springer.com/series/692
James Ramsay Giles Hooker •
Dynamic Data Analysis Modeling Data with Differential Equations
123
Giles Hooker Department of Biological Statistics and Computational Biology Cornell University Ithaca, NY USA
James Ramsay Department of Psychology McGill University Ottawa, ON Canada
ISSN 0172-7397 Springer Series in Statistics ISBN 978-1-4939-7188-6 DOI 10.1007/978-1-4939-7190-9
ISSN 2197-568X (electronic) ISBN 978-1-4939-7190-9
(eBook)
Library of Congress Control Number: 2017941073 © Springer Science+Business Media LLC 2017 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. 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. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Science+Business Media LLC The registered company address is: 233 Spring Street, New York, NY 10013, U.S.A.
Preface
Getting pregnant is usually easy and fun, but the gestation and delivery may be another story; messy and painful perhaps, but instructive nevertheless. So it is with this book, which began with enthusiasm and confidence, but ten or so years later the twists and turns along the way emerge as a key part of the story. Functional data analysis leads inevitably to dynamic systems. Ramsay and Silverman (2005) emphasized the reduction in bias and sampling variance that could be achieved by incorporating even an only approximately correct model into the penalty term by using a linear differential operator, thereby extending the more usual practice of defining roughness by the size of a high-order d
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