Statistical Analysis for High-Dimensional Data

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.

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

• 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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