Advertisement

New Advances in Statistics and Data Science

  • Ding-Geng Chen
  • Zhezhen Jin
  • Gang Li
  • Yi Li
  • Aiyi Liu
  • Yichuan Zhao

Part of the ICSA Book Series in Statistics book series (ICSABSS)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Review of Theoretical Framework in Data Science

    1. Front Matter
      Pages 1-1
    2. Marianthi Markatou, Yang Chen, Georgios Afendras, Bruce G. Lindsay
      Pages 3-26
    3. Georgios Afendras, Marianthi Markatou
      Pages 27-44
    4. Tapio Nummi, Jyrki Möttönen, Martti T. Tuomisto
      Pages 75-85
  3. Complex and Big Data Analysis

    1. Front Matter
      Pages 87-87
    2. Hongxiang Shi, Emily L. Kang, Bledar A. Konomi, Kumar Vemaganti, Sandeep Madireddy
      Pages 89-107
    3. Hongxiao Zhu, Ruijin Lu, Chen Ming, Anupam K. Gupta, Rolf Müller
      Pages 137-160
  4. Clinical Trials, Statistical Shape Analysis and Applications

  5. Statistical Modeling and Data Analysis

    1. Front Matter
      Pages 257-257
    2. Jianzhao Yang, Zhicheng Li, Xinyun Chen, Haipeng Xing
      Pages 259-276
    3. Shouhao Zhou, Chan Shen, J. Jack Lee
      Pages 277-291
    4. Tapio Nummi, Janne Salonen, Timothy E. O’Brien
      Pages 313-321
  6. Back Matter
    Pages 343-348

About this book

Introduction

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency, Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting  further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields.  The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.

Keywords

big data DNA statistical analysis clinical trials design functional data analysis gene expression analysis high dimensional statistical method longitudinal data analysis nonparametric statistics phylogeny-based kernels spline growth model statistical genetics and bioinformatics statistical methods statistical shape analysis survival data analysis uncertainty quantification

Editors and affiliations

  • Ding-Geng Chen
    • 1
  • Zhezhen Jin
    • 2
  • Gang Li
    • 3
  • Yi Li
    • 4
  • Aiyi Liu
    • 5
  • Yichuan Zhao
    • 6
  1. 1.University of North CarolinaChapel HillUSA
  2. 2.Columbia UniversityNew YorkUSA
  3. 3.University of CaliforniaLos AngelesUSA
  4. 4.University of Michigan-Ann ArborAnn ArborUSA
  5. 5.National Institutes of HealthBethesdaUSA
  6. 6.Georgia State UniversityAtlantaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-69416-0
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-69415-3
  • Online ISBN 978-3-319-69416-0
  • Series Print ISSN 2199-0980
  • Series Online ISSN 2199-0999
  • Buy this book on publisher's site
Industry Sectors
Pharma
Materials & Steel
Biotechnology
Finance, Business & Banking
Electronics
Telecommunications
Aerospace
Oil, Gas & Geosciences
Engineering