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Some Themes in High-Dimensional Statistics

  • Arnoldo Frigessi
  • Peter Bühlmann
  • Ingrid K. Glad
  • Sylvia Richardson
  • Marina Vannucci
Conference paper
Part of the Abel Symposia book series (ABEL, volume 11)

Abstract

The symposium covered a broad spectrum of themes on High-Dimensional Statistics. We present here a short overview of some of the topics discussed at the symposium: high-dimensional inference in regression, high-dimensional causal inference, Bayesian variable selection for high-dimensional analysis, and integration of multiple high-dimensional data, but this categorization is not exhaustive. The contributions by some of the participants, appearing as chapters in the book, include both in-depth reviews and development of new statistical methodology, applications and theory.

Keywords

Copy Number Alteration Dirichlet Process Group Lasso MCMC Algorithm Bayesian Variable Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Arnoldo Frigessi
    • 1
  • Peter Bühlmann
    • 2
  • Ingrid K. Glad
    • 3
  • Sylvia Richardson
    • 4
  • Marina Vannucci
    • 5
  1. 1.Oslo Centre for Biostatistics and EpidemiologyUniversity of OsloOsloNorway
  2. 2.Seminar for StatisticsETH ZürichZürichSwitzerland
  3. 3.Department of MathematicsUniversity of OsloOsloNorway
  4. 4.MRC Biostatistics Unit, Cambridge Institute of Public HealthUniversity of CambridgeCambridgeUK
  5. 5.Department of StatisticsRice UniversityHoustonUSA

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