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
ISBN: 0471852333, 9780471852339
Format: pdf
Publisher: Wiley

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. The first one, Outlier Detection: A Survey, is written by Chandola, Banerjee and Kumar. Tries to devise estimators that are not so strongly affected by outliers. Robust Regression and Outlier Detection. They define outlier detection as the problem of “[] finding patterns in data that do not conform to expected normal behavior“. Often, however, a transformation will not eliminate or attenuate the leverage of influential outliers that bias the prediction and distort the significance of parameter estimates. 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. Consequently, the literature on outliers is dispersed in statistics, process engineering and systems science as robust estimation, regression, system identification, and data analysis. Another useful survey article is “Robust statistics for outlier detection,” by Peter Rousseeuw and Mia Hubert. After an For example: neural networks, SVM, rule-based, clustering, nearest neighbors, regression, etc. "Robust Regression and Outlier Detection" states "robustregression .