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Theory and Methods (with R)

Then we give some asymptotic and finite sample minimax results for estimation and testing.

Robust Statistics

The material is complemented by miscellaneous remarks on: computational aspects; other estimates; scale, regression, time series and other estimation problems; some tentative practical recommendations. Source Ann. Zentralblatt MATH identifier JSTOR links.

Huber, Peter J.

Separating the Signal from the Noise: Robust Statistics for Pedestrians | R-bloggers

More by Peter J. Edited by M.

Hubert, G. Pison, A.

Rethinking Robust Statistics with Modern Bayesian Methods

Struyf and S. Van Aelst.

Robust Box-Cox transformations based on minimum residual autocorrelation. Computational Statistics and Data Analysis, 50, pp Consistency of the robust residual autocorrelation estimate of a transformation parameter.

Efficient Algorithms for High Dimensional Robust Learning

TRFMCE is a library of S functions for the computation of the robust response transformation based on optimal prediction. Journal of the American Statistical Association, , pp.

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IUMSP www. Software for robust statistics.

Z program sources for S-Plus 3. Binary files and interface for S-Plus 4. Unlike other books on the market, Robust Statistics: Theory and Methods with R offers the most comprehensive, definitive, and up-to-date treatment of the subject.


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It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. 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.

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It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences. KGaA - Betreiber - www. All rights reserved.