Statistical Analysis for High-Dimensional Data

The Abel Symposium 2014
Herausgegeben:Frigessi, Arnoldo; Bühlmann, Peter; Glad, Ingrid; Langaas, Mette; Richardson, Sylvia; Vannucci, Marina

• Gebundenes Buch

Produktbeschreibung

This book features research contributions fromThe Abel Symposium on Statistical Analysis for High Dimensional Data, held inNyvågar, Lofoten, Norway, in May 2014.

The focus of the symposium was on statisticaland machine learning methodologies specifically developed for inference in "bigdata" situations, with particular reference to genomic applications. Thecontributors, who are among the most prominent researchers on the theory ofstatistics for high dimensional inference, present new theories and methods, aswell as challenging applications and computational solutions. Specific themesinclude, among others, variable selection and screening, penalised regression,sparsity, thresholding, low dimensional structures, computational challenges,non-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testing,classification, factor models, clustering, and preselection.

Highlighting cutting-edge researchand casting light on future research directions, the contributions will benefitgraduate students and researchers in computational biology, statistics and themachine learning community.

Produktdetails

• Abel Symposia 11
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-27097-5
• 1st ed. 2016
• Seitenzahl: 320
• Erscheinungstermin: 17. Februar 2016
• Englisch
• Abmessung: 241mm x 160mm x 23mm
• Gewicht: 636g
• ISBN-13: 9783319270975
• ISBN-10: 3319270974
• Artikelnr.: 43959547

Inhaltsangabe

Some Themes in High-Dimensional Statistics: A. Frigessi et al.- LaplaceAppoximation in High-Dimensional Bayesian Regression: R. Barber, M. Drton etal.- Preselection in Lasso-Type Analysis for Ultra-High Dimensional GenomicExploration: L.C. Bergersen, I. Glad et al.- Spectral Clustering and Block Models:a Review and a new Algorithm: S. Bhattacharyya et al.- Bayesian HierarchicalMixture Models: L. Bottelo et al.- iBATCGH; Integrative Bayesian Analysis of Transcriptomicand CGH Data: Cassese, M. Vannucci et al.- Models of Random SparseEigenmatrices 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 Curvein Classification Problems: E. Laber et al.- Baysian Feature Allocation Modelsfor Tumor Heterogeneity: J. Lee, P. Mueller et al.- Bayesian Penalty Mixing:The Case of a Non-Separable Penalty: V. Rockova etal.- Confidence Intervalsfor Maximin Effects in Inhomogeneous Large Scale Data: D. Rothenhausler et al.-Chisquare Confidence Sets in High-Dimensional Regression: S. van de Geer et al.

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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 etal.- 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.