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Handbook of Big Data Analytics

  • Wolfgang Karl Härdle
  • Henry Horng-Shing Lu
  • Xiaotong Shen

Part of the Springer Handbooks of Computational Statistics book series (SHCS)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Overview

    1. Front Matter
      Pages 1-1
    2. John M. Jordan, Dennis K. J. Lin
      Pages 3-21
    3. Jing Shyr, Jane Chu, Mike Woods
      Pages 23-47
  3. Methodology

    1. Front Matter
      Pages 49-49
    2. Xinlian Zhang, Rui Xie, Ping Ma
      Pages 51-74
    3. Xiaoming Huo, Cheng Huang, Xuelei Sherry Ni
      Pages 75-102
    4. Hao Helen Zhang
      Pages 103-124
    5. James E. Gentle, Seunghye J. Wilson
      Pages 125-150
    6. Muting Wan, James G. Booth, Martin T. Wells
      Pages 151-201
    7. Wei Biao Wu, Zhipeng Lou, Yuefeng Han
      Pages 203-224
    8. Hui Zou
      Pages 225-261
    9. Hsiang-Ling Hsu, Ching-Kang Ing, Tze Leung Lai
      Pages 263-283
    10. Mark Vere Culp, Kenneth Joseph Ryan, George Michailidis
      Pages 285-299
    11. Qian Lin, Yang Li, Jun S. Liu
      Pages 301-323
    12. Yiwen Liu, Xin Xing, Wenxuan Zhong
      Pages 325-338
    13. Kevin Chen, H. T. Kung
      Pages 339-350
    14. Chien-Chang Chen, Hung-Hui Juan, Meng-Yuan Tsai, Henry Horng-Shing Lu
      Pages 351-374
  4. Software

    1. Front Matter
      Pages 375-375
    2. A Tutorial on Open image in new window: R Package for the Linearized Bregman Algorithm in High-Dimensional Statistics
      Jiechao Xiong, Feng Ruan, Yuan Yao
      Pages 425-453
  5. Application

    1. Front Matter
      Pages 455-455
    2. Pantelis Zenon Hadjipantelis, Hans-Georg Müller
      Pages 457-483
    3. Martin Bezener, Lynn E. Eberly, John Hughes, Galin Jones, Donald R. Musgrove
      Pages 485-501
    4. Franziska Göbel, Gilles Blanchard, Ulrike von Luxburg
      Pages 503-522
    5. Maciej Zieba, Wolfgang Karl Härdle
      Pages 523-538

About this book

Introduction

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 problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.  

Keywords

Big Data Computational Statistics Data Analytics High-Dimensional Data Analysis Software-Hardware Co-Designs Quantlet QuantLet

Editors and affiliations

  • Wolfgang Karl Härdle
    • 1
  • Henry Horng-Shing Lu
    • 2
  • Xiaotong Shen
    • 3
  1. 1.Ladislaus von Bortkiewicz Chair of Statistics, C.A.S.E. Center for Applied Statistics & EconomicsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Institute of StatisticsNational Chiao Tung UniversityHsinchuTaiwan
  3. 3.School of StatisticsUniversity of MinnesotaMinneapolisUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-18284-1
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-18283-4
  • Online ISBN 978-3-319-18284-1
  • Series Print ISSN 2197-9790
  • Series Online ISSN 2197-9804
  • Buy this book on publisher's site