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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.
List of contents
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.
Summary
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.
