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Computational Statistics Handbook with MATLAB

About Computational Statistics Handbook with MATLAB

A Strong Practical Focus on Applications and Algorithms Computational Statistics Handbook with MATLAB®, Third Edition covers today''s most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the implementation of the methods. New to the Third Edition This third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and multivariate adaptive regression splines. Web Resource The authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code, examples, and data sets are available online.

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  • Language:
  • English
  • ISBN:
  • 9781466592735
  • Binding:
  • Hardback
  • Pages:
  • 731
  • Published:
  • December 21, 2015
  • Edition:
  • 3
  • Dimensions:
  • 166x243x46 mm.
  • Weight:
  • 1214 g.
Delivery: 2-3 weeks
Expected delivery: January 11, 2025
Extended return policy to January 30, 2025
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Description of Computational Statistics Handbook with MATLAB

A Strong Practical Focus on Applications and Algorithms
Computational Statistics Handbook with MATLAB®, Third Edition covers today''s most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the implementation of the methods.
New to the Third Edition
This third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and multivariate adaptive regression splines.
Web Resource
The authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code, examples, and data sets are available online.

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