Statistical Analysis for High Dimensional Data

. Ed(s): Frigessi, Arnoldo; Buhlmann, Peter; Glad, Ingrid K.; Richardson, Sylvia; Vannucci, Marina; Langaas, Mette

ISBN 10: 3319270974 / ISBN 13: 9783319270975

Published by Springer International Publishing AG, 2016

Editor(s): Frigessi, Arnoldo; Buhlmann, Peter; Glad, Ingrid K.; Richardson, Sylvia; Vannucci, Marina; Langaas, Mette. Series: Abel Symposia. Num Pages: 318 pages, 46 black & white illustrations, 19 colour illustrations, 42 colour tables, biography. BIC Classification: PBKS; PBT; PSD; UFM. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 19. Weight in Grams: 643. . 2016. Hardback. . . . . Books ship from the US and Ireland. Seller Inventory # V9783319270975

Synopsis:

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.

From the Back Cover:

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.

Bibliographic Details

Title: Statistical Analysis for High Dimensional ...
Publisher: Springer International Publishing AG
Publication Date: 2016
Binding: Hardcover
Condition: New