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Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014 by Ingrid Glad, Sylvia Richardson, Mette Langaas, Marina Vannucci, Peter Buhlmann, Arnoldo Frigessi (Hardcover, 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.

Author
Ingrid Glad, Sylvia Richardson, Mette Langaas, Marina Vannucci, Peter Buhlmann, Arnoldo Frigessi
Publication Name
Statistical Analysis for High-Dimensional Data: the Abel Symposium 2014
Format
Hardcover
Language
English
Subject
Engineering & Technology, Computer Science, Biology, Mathematics, Healthcare System
Publication Year
2016
Type
Textbook
Number of Pages
306 Pages

Series Title
Abel Symposia
Country/Region of Manufacture
Switzerland
Editor
Marina Vannucci, Arnoldo Frigessi, Mette Langaas, Sylvia Richardson, Ingrid Glad, Peter Buhlmann

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