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Big and Complex Data Analysis

Methodologies and Applications

  • S. Ejaz Ahmed

Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. General High-Dimensional Theory and Methods

    1. Front Matter
      Pages 1-1
    2. Yangguang Zang, Qingzhao Zhang, Sanguo Zhang, Qizhai Li, Shuangge Ma
      Pages 29-50
    3. Gian-Andrea Thanei, Christina Heinze, Nicolai Meinshausen
      Pages 51-68
  3. Network Analysis and Big Data

    1. Front Matter
      Pages 121-121
    2. Ali Shojaie, Nafiseh Sedaghat
      Pages 159-192
    3. Kevin He, Yanming Li, Qingyi Wei, Yi Li
      Pages 193-207
    4. Christine Cutting, Davy Paindaveine, Thomas Verdebout
      Pages 209-227
    5. Stephen Bamattre, Rex Hu, Joseph S. Verducci
      Pages 229-246
  4. Statistics Learning and Applications

    1. Front Matter
      Pages 247-247
    2. Syed Ejaz Ahmed, Bahadır Yüzbaşı
      Pages 285-304
    3. Nicole Croteau, Farouk S. Nathoo, Jiguo Cao, Ryan Budney
      Pages 305-324
    4. Daniel A. Díaz-Pachón, Jean-Eudes Dazard, J. Sunil Rao
      Pages 325-345
    5. Guohua Wang, Yinjun Zhao, Qingzhao Zhang, Yangguang Zang, Sanguo Zang, Shuangge Ma
      Pages 347-367
    6. Sharon M. McNicholas, Paul D. McNicholas, Ryan P. Browne
      Pages 369-385

About this book

Introduction

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 for a host of useful models, as well as essential new developments in the field.

The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data.

The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

Keywords

big data analysis complex data analysis high-dimensional data analysis shrinkage estimation model selection estimation and prediction applications with real data sets

Editors and affiliations

  • S. Ejaz Ahmed
    • 1
  1. 1.Department of Mathematics & StatisticsBrock UniversitySt. CatherinesCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-41573-4
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-41572-7
  • Online ISBN 978-3-319-41573-4
  • Series Print ISSN 1431-1968
  • Buy this book on publisher's site