This book features research contributions from
The Abel Symposium on Statistical Analysis for High Dimensional Data, held in
Nyv?gar, 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.
