ENGLISH

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

By Sara van de Geer (auth.)

Book information

Publisher
Springer International Publishing
Year
2016
ISBN
978-3-319-32773-0, 978-3-319-32774-7
DOI
10.1007/978-3-319-32774-7
Language
english
Format
PDF
Filesize
3 MB (2693317 bytes)
Series
Lecture Notes in Mathematics 2159
Edition
1
Pages
XIII, 274\278
Time added
2016-07-20 04:00:00
Probability Theory and Stochastic Processes; Stati

Description

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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