Overview
- Broad spectrum of problems
- Cutting edge research
- Includes supplementary material: sn.pub/extras
Part of the book series: Abel Symposia (ABEL, volume 11)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (13 papers)
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
Bibliographic Information
Book Title: Statistical Analysis for High-Dimensional Data
Book Subtitle: The Abel Symposium 2014
Editors: Arnoldo Frigessi, Peter Bühlmann, Ingrid K. Glad, Mette Langaas, Sylvia Richardson, Marina Vannucci
Series Title: Abel Symposia
DOI: https://doi.org/10.1007/978-3-319-27099-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-27097-5Published: 17 February 2016
Softcover ISBN: 978-3-319-80073-8Published: 30 March 2018
eBook ISBN: 978-3-319-27099-9Published: 16 February 2016
Series ISSN: 2193-2808
Series E-ISSN: 2197-8549
Edition Number: 1
Number of Pages: XII, 306
Number of Illustrations: 19 b/w illustrations, 46 illustrations in colour
Topics: Computational Mathematics and Numerical Analysis, Statistical Theory and Methods, Bioinformatics, Statistics and Computing/Statistics Programs, Statistics for Life Sciences, Medicine, Health Sciences, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences