Robust Statistics: Theory and Methods (repost)

Description

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.

Review
"This book belongs on the desk of every statistician working in robust statistics, and the authors are to be congratulated for providing the profession with a much-needed and valuable resource for teaching and research." (Journal of the American Statistical Association, June 2008)

"…an original and valuable contribution…a source of inspiration for all those pursuing research in robust statistics." (Mathematical Reviews, 2007i)

"…a great book for graduate students as well as for applied scientists and data analysts." (MAA Reviews, February 14, 2007)

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