Computational Statistics Handbook with MATLAB (Chapman & Hall/CRC Computer Science & Data Analysis)

Wendy L. Martinez, Angel R. Martinez

ISBN 10: 1584885661 / ISBN 13: 9781584885665

Verlag: Taylor & Francis, 2007

Beschreibung

Neu Neuware, Importqualität, auf Lager, Sofortversand - The authors make computational statistics as accessible as possible by playing down theory and building an understanding of the algorithms used in a wide range of applications. This second edition recognizes the new functionality of the MATLAB statistics toolbox and other updates like the new plotting capabilities of Version 7.0. Bestandsnummer des Verkäufers INF1000242470

Bibliografische Details

Titel: Computational Statistics Handbook with ...

Verlag: Taylor & Francis

Erscheinungsdatum: 2007

Einband: Gebundene Ausgabe

Zustand: Neu

Auflage: 2. Auflage

Über diesen Titel

Inhaltsangabe:

Book by Martinez Wendy L Martinez Angel R

Reseña del editor:

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.