Big and Complex Data Analysis Methodologies and Applications
This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies f
- PDF / 12,138,318 Bytes
- 390 Pages / 439.42 x 683.15 pts Page_size
- 29 Downloads / 248 Views
S. Ejaz Ahmed Editor
Big and Complex Data Analysis Methodologies and Applications
Contributions to Statistics
More information about this series at http://www.springer.com/series/2912
S. Ejaz Ahmed Editor
Big and Complex Data Analysis Methodologies and Applications
123
Editor S. Ejaz Ahmed Department of Mathematics & Statistics Brock University St. Catherines, Ontario Canada
ISSN 1431-1968 Contributions to Statistics ISBN 978-3-319-41572-7 DOI 10.1007/978-3-319-41573-4
ISBN 978-3-319-41573-4 (eBook)
Library of Congress Control Number: 2017930198 © Springer International Publishing AG 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 International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
This book comprises a collection of research contributions toward high-dimensional data analysis. In this data-centric world, we are often challenged with data sets containing many predictors in the model at hand. In a host of situations, the number of predictors may very well exceed the sample size. Truly, many modern scientific investigations require the analysis of such data. There are a host of buzzwords in today’s data-centric world, especially in digital and print media. We encounter data in every walk of life, and for analytically and objectively minded people, data is everything. However, making sense of the data and extracting meaningful information from it may not be an easy task. Sometimes, we come across buzzwords such as big data, high-dimensional data, data visualization, data science, and open data without a proper definition of such words. The rapid growth in the size and scope of data sets in a host of disciplines has created a need for innovative statistical and computa
Data Loading...
