Computational Statistics Handbook with Matlab, 2nd edition (Chapman & Hall/Crc Computer Science & Data Analysis) (Chapman & Hall/Crc Computer Science & Data Analysis)

Martinez, Angel R.

ISBN 10: 1584885661 / ISBN 13: 9781584885665

Published by Chapman and Hall/CRC, 2007

About this Item

The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Seller Inventory # GOR008342151

Bibliographic Details

Title: Computational Statistics Handbook with ...

Publisher: Chapman and Hall/CRC

Publication Date: 2007

Binding: Hardback

Condition: Very Good

Edition: 2nd Edition

About this title

Synopsis:

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.

Review:

[T]his book is perfectly appropriate as a textbook for an introductory course on computational statistics. It covers many useful topics, which in combination with the well-documented code, make the underlying concepts easy to grasp by the students. Overall, this is a very nice book to be used in an undergraduate or Masters level computational statistics course. It would also prove useful to researchers in other fields that want to learn and implement quickly some advanced statistical techniques.