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

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

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. 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. In fitting regression line outliers can significantly change the slope. The outlier detection using leave-one-out principle might not work in cases where there are many outliers. 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. 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. Authors: Toward Coherent Object Detection And Scene Layout Understanding Robust RVM Regression Using Sparse Outlier Model. Robust Regression And Outlier Detection Wiley Series In Probability And Statistics (99.75) Robust Statistics: The Approach Based On Influence Functions. High Performance Object Detection by Collaborative Learning of Joint Ranking of Granule Features. In such cases when the errors are not normal, robust regression is one of the methods that one can use.