Estimation and testing under sparsity : École d'été de probabilités de Saint-Flour XLV -- 2015 /

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 v...

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Bibliographic Details

Main Author:	Geer, S. A. van de (Sara A.) (Author)
Corporate Author:	Ecole d'été de probabilités de Saint-Flour
Format:	Conference Proceeding Book
Language:	English
Published:	[Cham] Switzerland : Springer, [2016]
Series:	Lecture notes in mathematics (Springer-Verlag) ; 2159. Lecture notes in mathematics (Springer-Verlag). École d'été de probabilités de Saint-Flour.
Subjects:	Estimation theory. Inequalities (Mathematics)
Summary:	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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