Advances in Complex Data Modeling and Computational Methods in Statistics
The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analy
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Anna Maria Paganoni Piercesare Secchi Editors
Advances in Complex Data Modeling and Computational Methods in Statistics
Contributions to Statistics
More information about this series at http://www.springer.com/series/2912
Anna Maria Paganoni • Piercesare Secchi Editors
Advances in Complex Data Modeling and Computational Methods in Statistics
123
Editors Anna Maria Paganoni Dipartimento di Matematica Politecnico di Milano Milano Italy
Piercesare Secchi Dipartimento di Matematica Politecnico di Milano Milano Italy
ISSN 1431-1968 Contributions to Statistics ISBN 978-3-319-11148-3 ISBN 978-3-319-11149-0 (eBook) DOI 10.1007/978-3-319-11149-0 Springer Cham Heidelberg New York Dordrecht London Library of Congress Control Number: 2014955400 © Springer International Publishing Switzerland 2015 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)
Preface
Statistics is rapidly changing. Pushed by data generated by new technologies, statisticians are asked to create new models and methods for exploring variability in mathematical settings that are often very far from the familiar Euclidean environment. Functions, manifold data, images and shapes, network graphs and trees are examples of object data that are becoming more and more common in statistical applications bu
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