Estimation and Testing Under Sparsity - Ecole d'Ete de Probabilites de Saint-Flour XLV - 2015 (Paperback, 1st ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Ecole d'Ete de Probabilites de Saint-Flour, 2159
Release date:	July 2016
First published:	2016
Authors:	Sara Van De Geer
Dimensions:	235 x 155 x 21mm (L x W x T)
Format:	Paperback
Pages:	274
Edition:	1st ed. 2016
ISBN-13:	978-3-319-32773-0
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	3-319-32773-9
Barcode:	9783319327730

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