Hastie, Trevor

Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

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ISBN 13:
9780367738334
author:
Hastie, Trevor
format:
Paperback / softback
publisher:
CRC Press
language:
English
Publication Year:
2020
Pages:
367
Dimensions:
23.1 x 15.5 x 1.8 centimeters (0
Genre:
Business, Operations, Statistics,
Condition:
New
Availability:
Item usually sent within 10 working days
£51.23

Description

Statistical Learning with Sparsity: The Lasso and Generalizations presents methods for exploiting sparsity to recover underlying signals in data. Top experts in the field describe the lasso for linear regression, generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization.

The book covers a range of topics including matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing, providing a thorough treatment of sparse statistical modeling. It concludes with a survey of theoretical results for the lasso, making it an essential resource for data analysts, computer scientists, and theorists working with high-dimensional data.

With its focus on tackling problems in big data, this book shows how sparsity assumption allows us to extract useful patterns from large datasets. A comprehensive and up-to-date treatment of sparse statistical modeling makes it a valuable addition to any library.

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