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Computational Statistics Handbook with MATLAB, Third Edition

Computational Statistics Handbook with MATLAB, Third Edition PDF

Author: Wendy L. Martinez and Angel R. Martinez

Publisher: Chapman and Hall


Publish Date: December 22, 2015

ISBN-10: 1466592737

Pages: 759

File Type: PDF

Language: English

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Book Preface

It has been almost ten years since the second edition of the Computational Statistics Handbook with MATLAB® was published, and MATLAB has evolved greatly in that time. There were also some relevant topics in computational statistics that we always wanted to include, such as support vector machines and multivariate adaptive regression splines. So, we felt it was time for a new edition.

To use all of the functions and code described in this book, one needs to have the MATLAB Statistics and Machine Learning Toolbox®, which is a product of The MathWorks, Inc. The name of this toolbox was changed to the longer title to reflect the connection with machine learning approaches, like supervised and unsupervised learning (Chapters 10 and 11). We will keep to the shorter name of “MATLAB Statistics Toolbox” in this text for readability. We list below some of the major changes in the third edition.

• Chapter 10 has additional sections on support vector machines and nearest neighbor classifiers. We also added a brief description of naive Bayes classifiers.
• Chapter 12 was updated to include sections on stepwise regression, least absolute shrinkage and selection operator (lasso), ridge regression, elastic net, and partial least squares regression.
• Chapter 13 now has a section on multivariate adaptive regression splines.
• Spatial statistics is an area that uses many of the techniques covered in the text, but it is not considered part of computational statistics. So, we removed Chapter 15.
• The introduction to MATLAB given in Appendix A has been expanded and updated to reflect the new desktop environment, object-oriented programming, and more.
• The text has been updated for MATLAB R2015a and the corresponding version of the Statistics and Machine Learning Toolbox.

We retained the same philosophy and writing style used in the previous editions of the book. The theory is kept to a minimum and is included where it offers some insights to the data analyst. All MATLAB code, example files, and data sets are available for download at the CRC website for the book:

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