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Statistics for high-dimensional data [Documento electrónico] : methods, theory and applications / Peter Bühlmann, Sara van de Geer

Main Author: Bühlmann, PeterCoauthor: van de Geer, Sara, co-aut.Language: eng.Country: Germany.Publication: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: XVIII, 558 p.ISBN: 978-3-642-20192-9.Series: Springer Series in StatisticsSubject - Topical Name: Estatística matemática | Modelos lineares (Estatística) Online Resources:Click here to access online
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Item type Current library Collection Call number Copy number Status Date due Barcode
E-Books Biblioteca NOVA FCT Online Não Ficção QA276.SPR FCT 82507 (Browse shelf(Opens below)) 1 Available

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Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.

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