9783319270975 - statistical analysis for high-dimensional data: the abel symposium 2014: 11 (8 risultati)

Lingua: Inglese
Editore: Springer, 2016
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Statistical Analysis for High Dimensional Data
Frigessi, Arnoldo|Bühlmann, Peter|Glad, Ingrid K.|Langaas, Mette|Richardson, Sylvia|Vannucci, Marina
Lingua: Inglese
Editore: Springer International Publishing, 2016
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Lingua: Inglese
Editore: Springer, 2016
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Hardcover. Condizione: Brand New. 9.25x6.25x0.75 inches. In Stock.

Lingua: Inglese
Editore: Springer, 2016
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Lingua: Inglese
Editore: Springer, 2016
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Lingua: Inglese
Editore: Springer International Publishing Feb 2016, 2016
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 methodologi…es 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 regressionsparsity, thresholding, low dimensional structures, computational challengesnon-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testingclassification, 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. 320 pp. Englisch.

Lingua: Inglese
Editore: Springer, Palgrave Macmillan Feb 2016, 2016
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 s…pecifically developed for inference in ¿bigdatä 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 regressionsparsity, thresholding, low dimensional structures, computational challengesnon-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testingclassification, 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 320 pp. Englisch.

Lingua: Inglese
Editore: Palgrave Macmillan, 2016
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 sp…ecifically 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.