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Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014
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” situ...
Autores principales: | , , , , , |
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Lenguaje: | eng |
Publicado: |
Springer
2016
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-27099-9 http://cds.cern.ch/record/2137939 |
_version_ | 1780950034113626112 |
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author | Frigessi, Arnoldo Bühlmann, Peter Glad, Ingrid Langaas, Mette Richardson, Sylvia Vannucci, Marina |
author_facet | Frigessi, Arnoldo Bühlmann, Peter Glad, Ingrid Langaas, Mette Richardson, Sylvia Vannucci, Marina |
author_sort | Frigessi, Arnoldo |
collection | CERN |
description | 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. |
id | cern-2137939 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2016 |
publisher | Springer |
record_format | invenio |
spelling | cern-21379392021-04-22T06:42:44Zdoi:10.1007/978-3-319-27099-9http://cds.cern.ch/record/2137939engFrigessi, ArnoldoBühlmann, PeterGlad, IngridLangaas, MetteRichardson, SylviaVannucci, MarinaStatistical Analysis for High-Dimensional Data : The Abel Symposium 2014Mathematical Physics and MathematicsThis 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.Springeroai:cds.cern.ch:21379392016 |
spellingShingle | Mathematical Physics and Mathematics Frigessi, Arnoldo Bühlmann, Peter Glad, Ingrid Langaas, Mette Richardson, Sylvia Vannucci, Marina Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014 |
title | Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014 |
title_full | Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014 |
title_fullStr | Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014 |
title_full_unstemmed | Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014 |
title_short | Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014 |
title_sort | statistical analysis for high-dimensional data : the abel symposium 2014 |
topic | Mathematical Physics and Mathematics |
url | https://dx.doi.org/10.1007/978-3-319-27099-9 http://cds.cern.ch/record/2137939 |
work_keys_str_mv | AT frigessiarnoldo statisticalanalysisforhighdimensionaldatatheabelsymposium2014 AT buhlmannpeter statisticalanalysisforhighdimensionaldatatheabelsymposium2014 AT gladingrid statisticalanalysisforhighdimensionaldatatheabelsymposium2014 AT langaasmette statisticalanalysisforhighdimensionaldatatheabelsymposium2014 AT richardsonsylvia statisticalanalysisforhighdimensionaldatatheabelsymposium2014 AT vannuccimarina statisticalanalysisforhighdimensionaldatatheabelsymposium2014 |