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Statistical Analysis for High-Dimensional Data

The Abel Symposium 2014

  • Arnoldo Frigessi
  • Peter Bühlmann
  • Ingrid K. Glad
  • Mette Langaas
  • Sylvia Richardson
  • Marina Vannucci

Part of the Abel Symposia book series (ABEL, volume 11)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Arnoldo Frigessi, Peter Bühlmann, Ingrid K. Glad, Sylvia Richardson, Marina Vannucci
    Pages 1-13
  3. Rina Foygel Barber, Mathias Drton, Kean Ming Tan
    Pages 15-36
  4. Linn Cecilie Bergersen, Ismaïl Ahmed, Arnoldo Frigessi, Ingrid K. Glad, Sylvia Richardson
    Pages 37-66
  5. Sharmodeep Bhattacharyya, Peter J. Bickel
    Pages 67-90
  6. Leonardo Bottolo, Petros Dellaportas
    Pages 91-103
  7. Alberto Cassese, Michele Guindani, Marina Vannucci
    Pages 105-123
  8. Zhi-Ping Feng, Francois Collin, Terence P. Speed
    Pages 155-188
  9. Eric B. Laber, Kerby Shedden, Yang Yang
    Pages 189-209
  10. Juhee Lee, Peter Müller, Subhajit Sengupta, Kamalakar Gulukota, Yuan Ji
    Pages 211-232
  11. Veronika Ročková, Edward I. George
    Pages 233-254
  12. Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann
    Pages 255-277
  13. Sara van de Geer, Benjamin Stucky
    Pages 279-306

About these proceedings

Introduction

This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014.

The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection.

Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.

Keywords

dimension reduction sparsity statistical genomics statistical inference in high dimensions high dimensional inference penelised regression thresholding multiple testing factor models

Editors and affiliations

  • Arnoldo Frigessi
    • 1
  • Peter Bühlmann
    • 2
  • Ingrid K. Glad
    • 3
  • Mette Langaas
    • 4
  • Sylvia Richardson
    • 5
  • Marina Vannucci
    • 6
  1. 1.Oslo Centre for Biostatistics and EpideUniversity of OsloOsloNorway
  2. 2.Seminar for StatisticsETH ZürichZürichSwitzerland
  3. 3.Department of MathematicsUniversity of OsloOsloNorway
  4. 4.Norwegian University of Science and TecDepartment of Mathematical SciencesTrondheimNorway
  5. 5.University of CambridgeMRC Biostatistics Unit, Cambridge InstitCambridgeUnited Kingdom
  6. 6.Department of StatisticsRice UniversityHoustonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-27099-9
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-27097-5
  • Online ISBN 978-3-319-27099-9
  • Series Print ISSN 2193-2808
  • Series Online ISSN 2197-8549
  • Buy this book on publisher's site
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