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Robust regression and outlier detection by Annick M. Leroy, Peter J. Rousseeuw

Robust regression and outlier detection Annick M. Leroy, Peter J. Rousseeuw ebook
Page: 347
Publisher: Wiley
Format: pdf
ISBN: 0471852333, 9780471852339

For data reconciliation, the SV regression Moreover, it is not so strict to tune the coefficients of the SV regression approach because of the robustness of the coefficients for the reconciled results. Even if you exclude CCSM4 as an outlier the contribution to the trend since '96 from volcanic recovery in the model ensemble is about the same as the observed trend. We further extend the sparse regression algorithm to a robust sparse regression algorithm for outlier detection, which provides superior accuracy compared to the traditional IQR method. WILEY–INTERSCIENCE PAPERBACK SERIES The Wiley–Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. Robust Regression And Outlier Detection Wiley Series In Probability And Statistics (99.75) Robust Statistics: The Approach Based On Influence Functions. Here is a plot of post 96 observations . I encountered a wonderful survey article, "Robust statistics for outlier detection," by Peter Rousseeuw and Mia Hubert. The detection of outliers before analyzing the data analysis is not done then it may lead to model misspecification, biased parameter estimation and incorrect results. The volcanic term is critical here – at the moment it looks to me as though the ENSO term is robust, and the solar fairly robust (and small) once a response function is included. In fitting regression line outliers can significantly change the slope. Furthermore, a support vector regression (SV regression) approach is proposed for simultaneous data reconciliation and gross error or outlier detection, which considers gross errors and outliers as model complexity so as to remove them.