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Springer Information Criteria and Statistical Modeling Book

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£105.21 £60.32 £70.11 £79.91 £89.70 £99.50 £109.29 26 July 2026 16 August 2026 07 September 2026 29 September 2026 21 October 2026
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Description

Master the fundamentals of model selection with Information Criteria and Statistical Modeling from the Springer Series in Statistics. This book focuses on the Akaike information criterion (AIC), which serves as an estimator of the Kullback-Leibler information discrepancy. It provides a detailed look at how the AIC is used as a tool for evaluating statistical models across various fields, including engineering, social sciences, and natural sciences. Readers will gain a deep understanding of the concepts and derivations behind the AIC and related criteria, such as Schwarz's Bayesian information criterion (BIC). Through a wide range of practical examples, this text covers model selection and evaluation processes, making it a valuable resource for anyone working with complex data mining and statistical modeling tasks.

Key Features

This Springer publication provides comprehensive explanations of the Akaike information criterion (AIC) and its derivations.

Learn about Schwarz's Bayesian information criterion (BIC) and how it relates to other model selection tools.

Explore practical examples of model selection and evaluation criteria used in natural sciences, social sciences, and engineering.

The text examines the AIC as an estimator of the Kullback-Leibler information discrepancy for evaluating statistical models.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
12 October 2007
Listed Since
07 May 2007

Barcode

No barcode data available

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