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.

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