Handbook of Big Data Analytics
Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional prob
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Wolfgang Karl Härdle Henry Horng-Shing Lu Xiaotong Shen Editors
Handbook of Big Data Analytics
Springer Handbooks of Computational Statistics
Series editors James E. Gentle Wolfgang K. Härdle Yuichi Mori
More information about this series at http://www.springer.com/series/7286
Wolfgang Karl HRardle • Henry Horng-Shing Lu • Xiaotong Shen Editors
Handbook of Big Data Analytics
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Editors Wolfgang Karl HRardle Ladislaus von Bortkiewicz Chair of Statistics C.A.S.E. Center for Applied Statistics & Economics Humboldt-UniversitRat zu Berlin Berlin, Germany
Henry Horng-Shing Lu Institute of Statistics National Chiao Tung University Hsinchu, Taiwan
Xiaotong Shen School of Statistics University of Minnesota Minneapolis, USA
ISSN 2197-9790 ISSN 2197-9804 (electronic) Springer Handbooks of Computational Statistics ISBN 978-3-319-18283-4 ISBN 978-3-319-18284-1 (eBook) https://doi.org/10.1007/978-3-319-18284-1 Library of Congress Control Number: 2018948165 © Springer International Publishing AG, part of Springer Nature 2018 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 the registered company Springer International Publishing AG part of Springer Nature. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
A tremendous growth of high-throughput techniques leads to huge data collections that are accumulating in an exponential speed with high volume, velocity, and variety. This creates the challenges of big data analytics for statistical science. These challenges demand creative innovation of statistical methods and smart computational Quantlets, macros, and programs to capture the often genuinely sparse informational content of huge unstructured data. Particularly, the development of analytic methodologies must take into account the co-design of hardware and
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