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. They offer all MATLAB code, example files, and data sets available for download online. Updated for MATLAB R2007a and the Statistics Toolbox, Version 6.0, this edition incoporates many additional computational statistics topics. - New functions for multivariate normal and multivariate t distributions - Updated information on the new MATLAB functionally 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, quadaratic classifiers, and voting methods, such as bagging, boosting, and random forests - More methods for unsupervised learning, including model-based clustering and techniques for assesing the results of clustering - A new chapter on parametric models that covers spline regression models, logistic regression, and generalized linear models - Expanded information on smoothes, such as bin smooothing, running mean and line smoothers, and smoothing splines.
