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Statistical analysis for high-dimens...

Frigessi, Arnoldo.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical analysis for high-dimensional data/ edited by Arnoldo Frigessi ... [et al.].
其他題名:	The Abel Symposium 2014 /
其他作者:	Frigessi, Arnoldo.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xii, 306 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Mathematical statistics -
電子資源:	http://dx.doi.org/10.1007/978-3-319-27099-9
ISBN:	9783319270999$q(electronic bk.)

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$q(electronic bk.)

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

646437
Mathematical statistics

LC Class. No.: QA276.A1

Dewey Class. No.: 519.5

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