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Statistical Analysis for High-Dimensional Data

The Abel Symposium 2014

  • Conference proceedings
  • © 2016

Overview

Editors:
  1. Arnoldo Frigessi

    1. Oslo Centre for Biostatistics and Epide, University of Oslo, Oslo, Norway

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  2. Peter Bühlmann

    1. Seminar for Statistics, ETH Zürich, Zürich, Switzerland

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  3. Ingrid K. Glad

    1. Department of Mathematics, University of Oslo, Oslo, Norway

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  4. Mette Langaas

    1. Norwegian University of Science and Tec, Department of Mathematical Sciences, Trondheim, Norway

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  5. Sylvia Richardson

    1. University of Cambridge, MRC Biostatistics Unit, Cambridge Instit, Cambridge, United Kingdom

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  6. Marina Vannucci

    1. Department of Statistics, Rice University, Houston, USA

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  • Broad spectrum of problems
  • Cutting edge research
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (13 papers)

  1. Front Matter

    Pages i-xii
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  2. Some Themes in High-Dimensional Statistics

    • Arnoldo Frigessi, Peter Bühlmann, Ingrid K. Glad, Sylvia Richardson, Marina Vannucci
    Pages 1-13

  3. Laplace Approximation in High-Dimensional Bayesian Regression

    • Rina Foygel Barber, Mathias Drton, Kean Ming Tan
    Pages 15-36

  4. Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration

    • Linn Cecilie Bergersen, Ismaïl Ahmed, Arnoldo Frigessi, Ingrid K. Glad, Sylvia Richardson
    Pages 37-66

  5. Spectral Clustering and Block Models: A Review and a New Algorithm

    • Sharmodeep Bhattacharyya, Peter J. Bickel
    Pages 67-90

  6. Bayesian Hierarchical Mixture Models

    • Leonardo Bottolo, Petros Dellaportas
    Pages 91-103

  7. iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH Data

    • Alberto Cassese, Michele Guindani, Marina Vannucci
    Pages 105-123

  8. Models of Random Sparse Eigenmatrices and Bayesian Analysis of Multivariate Structure

    • Andrew Cron, Mike West
    Pages 125-153

  9. Combining Single and Paired End RNA-seq Data for Differential Expression Analyses

    • Zhi-Ping Feng, Francois Collin, Terence P. Speed
    Pages 155-188

  10. An Imputation Method for Estimating the Learning Curve in Classification Problems

    • Eric B. Laber, Kerby Shedden, Yang Yang
    Pages 189-209

  11. Bayesian Feature Allocation Models for Tumor Heterogeneity

    • Juhee Lee, Peter Müller, Subhajit Sengupta, Kamalakar Gulukota, Yuan Ji
    Pages 211-232

  12. Bayesian Penalty Mixing: The Case of a Non-separable Penalty

    • Veronika Ročková, Edward I. George
    Pages 233-254

  13. Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data

    • Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann
    Pages 255-277

  14. χ 2-Confidence Sets in High-Dimensional Regression

    • Sara van de Geer, Benjamin Stucky
    Pages 279-306
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Keywords

About this book

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.

Editors and Affiliations

  • Oslo Centre for Biostatistics and Epide, University of Oslo, Oslo, Norway

    Arnoldo Frigessi

  • Seminar for Statistics, ETH Zürich, Zürich, Switzerland

    Peter Bühlmann

  • Department of Mathematics, University of Oslo, Oslo, Norway

    Ingrid K. Glad

  • Norwegian University of Science and Tec, Department of Mathematical Sciences, Trondheim, Norway

    Mette Langaas

  • University of Cambridge, MRC Biostatistics Unit, Cambridge Instit, Cambridge, United Kingdom

    Sylvia Richardson

  • Department of Statistics, Rice University, Houston, USA

    Marina Vannucci

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