Robust Statistics

Theory and Methods

Ricardo Maronna, Doug Martin, Victor Yohai

408 pages, parution le 24/03/2006

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Résumé

Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and different applications considered.

Robust Statistics sets out to explain the use of robust methods and their theoretical justification. It provides an up-to-date overview of the theory and practical application of the robust statistical methods in regression, multivariate analysis, generalized linear models and time series. This unique book:

Enables the reader to select and use the most appropriate robust method for their particular statistical model.
Features computational algorithms for the core methods.
Covers regression methods for data mining applications.
Includes examples with real data and applications using the S-Plus robust statistics library.
Describes the theoretical and operational aspects of robust methods separately, so the reader can choose to focus on one or the other.
Supported by a supplementary website featuring time-limited S-Plus download, along with datasets and S-Plus code to allow the reader to reproduce the examples given in the book.

Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is ideal for researchers, practitioners and graduate students of statistics, electrical, chemical and biochemical engineering, and computer vision. There is also much to benefit researchers from other sciences, such as biotechnology, who need to use robust statistical methods in their work.

Sommaire

Location and Scale
Measuring Robustness
Linear Regression 1
Linear Regression 2
Multivariate Analysis
Generalized Linear Models
Time Series
Numerical Algorithms
Asymptotic Theory of M-estimates
Robust Methods in S-Plus
Description of Data Sets

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Ricardo Maronna, Doug Martin, Victor Yohai
Parution	24/03/2006
Nb. de pages	408
Format	16 x 23,5
Couverture	Relié
Poids	740g
Intérieur	Noir et Blanc
EAN13	9780470010921
ISBN13	978-0-470-01092-1