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

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

(2003), The Impact of Trade on Intra-Industry Reallocations and. 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. The basis of the algorithm is Peter J. In such cases when the errors are not normal, robust regression is one of the methods that one can use. What is new is that MathWorks addded a wide set of support functions that simplify common analysis tasks like plotting, outlier detection, generating predictions, performing stepwise regression, applying robust regression. Robust Regression and Outlier Detection (Wiley Series in Probability and Statistics) book download. Robust regression and outlier detection. Leroy (1987), Robust Regression and Outlier. Tuesday, 9 April 2013 at 13:07. 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. Econometrica 71 (6), 1695-1725. The least squared regression with the lowest meadian squared error is chosen as the final model. The outlier detection using leave-one-out principle might not work in cases where there are many outliers. Aggregate Industry Productivity.