Robust Statistics: Theory and Methods: 855 (Wiley Series in Probability and Statistics) - Hardcover

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Ricardo Maronna is a Professor in the Department of Mathematics, Faculty of Exact Sciences, National University of La Plata, Argentina, and researcher at C.I.C.P.B.A. He is the author of numerous research articles on robust statistics, especially in the areas of regression and multivariate analysis.

Doug Martin is a Professor in the Department of Statistics, and Director of the Computational Finance Program at the University of Washington in Seattle, Washington. He was a consultant at Bell Laboratories for many years, and author of numerous research articles on robust methods for time series. Martin founded the original S-PLUS company Statistical Sciences, Inc., and led the development of the S-PLUS Robust Statistics Library.

Victor Yohai, is a Professor in the Department of Mathematics, Faculty of Exact and Natural Sciences, University of Buenos Aires, Argentina, and researcher at CONICET. He is the author of a large number of important research articles on robust statistics, in particular on regression and time series. Several of the procedures proposed by him have been implemented in the robust library of S-PLUS.

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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.