Statistical Analysis for High-dimensional Data: The Abel Symposium 2014: 11 - Rilegato

Rilegato

ISBN 10: 3319270974 ISBN 13: 9783319270975

Casa editrice: Springer Nature, 2016

Riassunto

This book features research contributions fromThe Abel Symposium on Statistical Analysis for High Dimensional Data, held inNyvågar, Lofoten, Norway, in May 2014.

The focus of the symposium was on statisticaland machine learning methodologies specifically developed for inference in “bigdata” situations, with particular reference to genomic applications. Thecontributors, who are among the most prominent researchers on the theory ofstatistics for high dimensional inference, present new theories and methods, aswell as challenging applications and computational solutions. Specific themesinclude, among others, variable selection and screening, penalised regression,sparsity, thresholding, low dimensional structures, computational challenges,non-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testing,classification, factor models, clustering, and preselection.

Highlighting cutting-edge researchand casting light on future research directions, the contributions will benefitgraduate students and researchers in computational biology, statistics and themachine learning community.

Su questo libro

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3319270974
• ISBN 13: 9783319270975
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 320
• Redattore: Frigessi Arnoldo, Bhlmann Peter, Glad Ingrid K., Langaas Mette, Richardson Sylvia
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania