Peter J.Huber – Robust Statistics

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Author: Peter J.Huber

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Robust Statistics

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From the Publisher

The first systematic, book-length treatment of the subject. Begins with a general introduction and the formal mathematical background behind qualitative and quantitative robustness. Stresses concepts. Provides selected numerical algorithms for computing robust estimates, as well as convergence proofs. Tables contain quantitative robustness information for a variety of estimates.

From the Inside Flap

Although several leading scientists in the late nineteenth and early twentieth centuries possessed a clear, operational understanding of the idea of robust statistics, the field was not recognized as a legitimate area of investigation until the mid-1960s. Briefly, a statistical method that exhibits an insensitivity to deviation from its own assumptions, is robust. The present volume represents the first systematic, book-length exposition of the subject. The treatment here is theoretical, with the stress on concepts rather than on extensive mathematical completeness.

From the Back Cover

Other volumes in the Wiley Series in Probability and Mathematical Statistics Abstract Inference UIf Grenander The traditional setting of statistical inference is when both sample space and parameter space are finite dimensional Euclidean spaces or subjects of such spaces. During the last decades, however, a theory has been developed that allows the sample space to be some abstract space.

About the Author

About the author PETER J. HUBER is Professor of Statistics at Harvard University, a position he has held since 1978. From 1964 to 1978 he was Professor of Statistics at ETH Zurich. Dr. Huber received his Ph.D. in mathematics from ETH Zurich in 1961.
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