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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Produktdetaljer
ISBN
9783319270999
Publisert
2018
Utgiver
Vendor
Springer
Språk
Product language
Engelsk
Format
Product format
Digital bok