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

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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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Detail Information

Series Title

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Call Number

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Publisher

: ., 2016

Collation

XIII, 274

Language

English

ISBN/ISSN

978-3-319-32774-7

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Statistical Theory and Methods
Probability Theory
Probability and Statistics in Computer Science

Specific Detail Info

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Statement of Responsibility

Sara van de Geer

Other Information

Cataloger

agus

Source

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