Computational Statistics Handbook with MATLAB, Second Edition

Wendy L. Martinez, Angel R. Martinez

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$83.96

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December 20, 2007 by Chapman and Hall/CRC
Textbook - 792 Pages - 211 B/W Illustrations
ISBN 9781584885665 - CAT# C5661
Series: Chapman & Hall/CRC Computer Science & Data Analysis

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Features

  • Provides MATLAB code for various algorithms to help readers understand important statistical concepts
  • Presents the basic functions of MATLAB, making the book accessible to all users
  • Includes MATLAB graphical interfaces for algorithms and demos, such as brushing, spinning, and grand tour
  • Supplies pseudocode so readers can implement algorithms using other software packages
  • Offers all MATLAB code, example files, and data sets available for download online
  • Summary

    As with the bestselling first edition, Computational Statistics Handbook with MATLAB®, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of the algorithms in data analysis. Updated for MATLAB® R2007a and the Statistics Toolbox, Version 6.0, this edition incorporates many additional computational statistics topics.

    New to the Second Edition

    •          New functions for multivariate normal and multivariate t distributions

    •          Updated information on the new MATLAB functionality for univariate and bivariate histograms, glyphs, and parallel coordinate plots

    •          New content on independent component analysis, nonlinear dimensionality reduction, and multidimensional scaling

    •          New topics on linear classifiers, quadratic classifiers, and voting methods, such as bagging, boosting, and random forests

    •          More methods for unsupervised learning, including model-based clustering and techniques for assessing the results of clustering

    •          A new chapter on parametric models that covers spline regression models, logistic regression, and generalized linear models

    •          Expanded information on smoothers, such as bin smoothing, running mean and line smoothers, and smoothing splines

    With numerous problems and suggestions for further reading, this accessible text facilitates an understanding of computational statistics concepts and how they are employed in data analysis.