Estimation and Testing Under Sparsity: École d'Été de Probabilités de Saint-Flour XLV – 2015 - Softcover

Van De Geer, Sara

 
9783319327730: Estimation and Testing Under Sparsity: École d'Été de Probabilités de Saint-Flour XLV – 2015
Softcover
ISBN 10: 3319327739 ISBN 13: 9783319327730
Verlag: Springer, 2016

Inhaltsangabe

Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.

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Über die Autorin bzw. den Autor

Peter Bühlmann is Professor of Statistics at ETH Zürich. His main research areas are high-dimensional statistical inference, machine learning, graphical modeling, nonparametric methods, and statistical modeling in the life sciences. He is currently editor of the Annals of Statistics. He was awarded a Medallion lecture by the Institute of Mathematical Statistics in 2009 and read a paper to the Royal Statistical Society in 2010. Sara van de Geer has been a full professor at the ETH in Zürich since 2005. Her main areas of research are empirical process theory, statistical learning theory, and nonparametric and high-dimensional statistics. She is an associate editor of Probability Theory and Related Fields, The Scandinavian Journal of Statistics and Statistical Surveys and a member of the Swiss National Science Foundation and correspondent of the Dutch Royal Academy of Sciences. She received the IMS medal in 2003 and the ISI award in 2005, and was an invited speaker at the International Conference of Mathematicians in 2010.

Von der hinteren Coverseite

Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.


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Verlag
Springer
Erscheinungsdatum
2016
Sprache
Englisch
ISBN 10 
3319327739
ISBN 13 
9783319327730
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
292
Kontakt zum Hersteller
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Darmstadt
64293
Deutschland

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ISBN 10:  3319327755 ISBN 13:  9783319327754
Verlag: Springer, 2016
Softcover