Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Frigessi, Arnoldo, Bühlmann, Peter, Glad, Ingrid, Langaas, Mette, Richardson, Sylvia, Vannucci, Marina
Lenguaje:eng
Publicado: Springer 2016
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-27099-9
http://cds.cern.ch/record/2137939
_version_ 1780950034113626112
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