Statistical Analysis for High-Dimensional Data - The Abel Symposium 2014 (Hardcover, 1st ed. 2016)

This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvagar, 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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Abel Symposia, 11
Release date:	February 2016
First published:	2016
Editors:	Arnoldo Frigessi • Peter Buhlmann • Ingrid Glad • Mette Langaas • Sylvia Richardson • Marina Vannucci
Dimensions:	235 x 155 x 19mm (L x W x T)
Format:	Hardcover
Pages:	306
Edition:	1st ed. 2016
ISBN-13:	978-3-319-27097-5
Categories:	Books > Science & Mathematics > Mathematics > Numerical analysis Books > Science & Mathematics > Mathematics > Probability & statistics Books > Science & Mathematics > Biology, life sciences > Molecular biology Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software Promotions
LSN:	3-319-27097-4
Barcode:	9783319270975

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