Produktnummer:
18a38b6b2092c94423bedf3b1acb2e818b
Themengebiete: | dimension reduction factor models high dimensional inference multiple testing penelised regression sparsity statistical genomics statistical inference in high dimensions thresholding |
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Veröffentlichungsdatum: | 30.03.2018 |
EAN: | 9783319800738 |
Sprache: | Englisch |
Seitenzahl: | 306 |
Produktart: | Kartoniert / Broschiert |
Herausgeber: | Bühlmann, Peter Frigessi, Arnoldo Glad, Ingrid Langaas, Mette Richardson, Sylvia Vannucci, Marina |
Verlag: | Springer International Publishing |
Untertitel: | The Abel Symposium 2014 |
Produktinformationen "Statistical Analysis for High-Dimensional Data"
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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