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Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014

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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.

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Series Title

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Call Number

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Publisher

: Springer., 2016

Collation

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Language

English

ISBN/ISSN

978-3319270975

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

-

Edition

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Subject(s)

Statistical
Dimensional Data

Specific Detail Info

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Statement of Responsibility

Arnoldo Frigessi

Other Information

Cataloger

Suwardi

Source

https://www.amazon.com/Statistical-Analysis-High-Dimensional-Data-Symposium/dp/3319270974

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