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Springer Statistics for High-Dimensional Data Textbook

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Description

Modern statistics requires navigating large and complex data sets with models containing numerous parameters. This Springer Series in Statistics book provides a detailed account of recently developed approaches to handle these challenges. Readers gain access to in-depth coverage of the Lasso and its various model versions, boosting methods, and undirected graphical modeling. The text also includes procedures for controlling false positive selections. By combining comprehensive mathematical theory with practical methodology, algorithms, and real data examples, this book bridges the gap between theoretical high-dimensional statistics and actual application. It serves as a complete resource for understanding the mathematical foundations and the algorithmic implementations necessary for modern data science.

Key Features

This Springer book covers modern statistical approaches for large and complex data sets.

The text provides detailed accounts of the Lasso and various versions of it for different models.

Includes technical information on boosting methods and undirected graphical modeling.

Provides specific procedures for controlling false positive selections in high-dimensional settings.

Combines mathematical theory with practical algorithms and real data examples for application.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
08 June 2011
Listed Since
25 February 2011

Barcode

No barcode data available

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