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Statistical analysis for high-dimensional data = The Abel Symposium 2014 /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical analysis for high-dimensional data/ edited by Arnoldo Frigessi ... [et al.].
Reminder of title:	The Abel Symposium 2014 /
other author:	Frigessi, Arnoldo.
corporate name:	Workshop on the Preservation of Stability under Discretization
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xii, 306 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Mathematical statistics -
Online resource:	http://dx.doi.org/10.1007/978-3-319-27099-9
ISBN:	9783319270999

Statistical analysis for high-dimensional data = The Abel Symposium 2014 /
Statistical analysis for high-dimensional dataThe Abel Symposium 2014 /[electronic resource] :edited by Arnoldo Frigessi ... [et al.]. - Cham :Springer International Publishing :2016. - xii, 306 p. :ill., digital ;24 cm. - Abel symposia,112193-2808 ;. - Abel symposia ;9..

Some Themes in High-Dimensional Statistics: A. Frigessi et al -- Laplace Appoximation in High-Dimensional Bayesian Regression: R. Barber, M. Drton et al -- Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration: L.C. Bergersen, I. Glad et al -- Spectral Clustering and Block Models: a Review and a new Algorithm: S. Bhattacharyya et al -- Bayesian Hierarchical Mixture Models: L. Bottelo et al -- iBATCGH; Integrative Bayesian Analysis of Transcriptomic and CGH Data: Cassese, M. Vannucci et al -- Models of Random Sparse Eigenmatrices and Bayesian Analysis of Multivariate Structure: A.J. Cron, M. West -- Combining Single and Paired End RNA-seq Data for Differential Expression Analysis: F. Feng, T.Speed et al -- An Imputation Method for Estimation the Learning Curve in Classification Problems: E. Laber et al -- Baysian Feature Allocation Models for Tumor Heterogeneity: J. Lee, P. Mueller et al -- Bayesian Penalty Mixing: The Case of a Non-Separable Penalty: V. Rockova et al -- Confidence Intervals for Maximin Effects in Inhomogeneous Large Scale Data: D. Rothenhausler et al -- Chisquare Confidence Sets in High-Dimensional Regression: S. van de Geer et al.

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.

ISBN: 9783319270999

Standard No.: 10.1007/978-3-319-27099-9doiSubjects--Topical Terms:

682993
Mathematical statistics

LC Class. No.: QA276.A1

Dewey Class. No.: 519.5

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520 $a 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.
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