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Einband grossStatistical Learning with Sparsity
ISBN/GTIN

Statistical Learning with Sparsity

E-BookPDFDRM AdobeE-Book
367 Seiten
Englisch
Taylor & Franciserschienen am07.05.20151. Auflage
In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. The authors cover the lasso for linear regression, generalized penalties, numerical methods for optimization, statistical inference methods for fitted (lasso) models, sparse multivariate analysis, graphical models, compressed sensing, and much more.mehr
Verfügbare Formate
BuchGebunden
EUR132,50
TaschenbuchKartoniert, Paperback
EUR56,00
E-BookPDFDRM AdobeE-Book
EUR53,49

Produkt

KlappentextIn this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. The authors cover the lasso for linear regression, generalized penalties, numerical methods for optimization, statistical inference methods for fitted (lasso) models, sparse multivariate analysis, graphical models, compressed sensing, and much more.
Details
Weitere ISBN/GTIN9781498712170
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format HinweisDRM Adobe
Erscheinungsjahr2015
Erscheinungsdatum07.05.2015
Auflage1. Auflage
Seiten367 Seiten
SpracheEnglisch
Dateigrösse12335 Kbytes
Artikel-Nr.4648786
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
Introduction. The Lasso for Linear Models. Generalized Linear Models. Generalizations of the Lasso Penalty. Optimization Methods. Statistical Inference. Matrix Decompositions, Approximations, and Completion. Sparse Multivariate Methods. Graphs and Model Selection. Signal Approximation and Compressed Sensing. Theoretical Results for the Lasso. Bibliography. Author Index. Index.mehr

Autor

Trevor Hastie is the John A. Overdeck Professor of Statistics at Stanford University. Prior to joining Stanford University, Professor Hastie worked at AT&T Bell Laboratories, where he helped develop the statistical modeling environment popular in the R computing system. Professor Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics, and machine learning. He has published five books and over 180 research articles in these areas. In 2014, he received the Emanuel and Carol Parzen Prize for Statistical Innovation. He earned a PhD from Stanford University.

Robert Tibshirani is a professor in the Departments of Statistics and Health Research and Policy at Stanford University. He has authored five books, co-authored three books, and published over 200 research articles. He has made important contributions to the analysis of complex datasets, including the lasso and significance analysis of microarrays (SAM). He also co-authored the first study that linked cell phone usage with car accidents, a widely cited article that has played a role in the introduction of legislation that restricts the use of phones while driving. Professor Tibshirani was a recipient of the prestigious COPSS Presidents' Award in 1996 and was elected to the National Academy of Sciences in 2012.

Martin Wainwright is a professor in the Department of Statistics and the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Professor Wainwright is known for theoretical and methodological research at the interface between statistics and computation, with particular emphasis on high-dimensional statistics, machine learning, graphical models, and information theory. He has published over 80 papers and one book in these areas, received the COPSS Presidents' Award in 2014, and was a section lecturer at the Interna