Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014

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Arnoldo Frigessi, Peter Bühlmann, Ingrid Glad, Mette Langaas, Sylvia Richardson, Marina Vannucci
Springer, Feb 16, 2016 - Mathematics - 306 pages

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.


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Some Themes in HighDimensional Statistics
Laplace Approximation in HighDimensional Bayesian Regression
Preselection in LassoType Analysis for UltraHigh Dimensional Genomic Exploration
A Reviewand a New Algorithm
Bayesian Hierarchical Mixture Models
Integrative Bayesian Analysis of Transcriptomic and CGH Data
Models of Random Sparse Eigenmatrices and Bayesian Analysis of Multivariate Structure
Combining Single and Paired End RNAseq Data for Differential Expression Analyses
An Imputation Method for Estimating the Learning Curve in Classification Problems
Bayesian Feature Allocation Models for Tumor Heterogeneity
The Case of a Nonseparable Penalty
Confidence Intervals for Maximin Effects in Inhomogeneous LargeScale Data
χ2Confidence Sets in HighDimensional Regression

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