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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
Publisher: Wiley
Format: pdf
Page: 347
ISBN: 0471852333, 9780471852339

Bjoerck A, Numerical methods for least squares problems. 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. Leroy, “Robust regression and outlier detection”, John Wiley &. There are also methods for linear regression which are resistant to the presence of outliers, which fall into the category of robust regression. The implemented statistical method will be based on robust PCA [3], which decomposes a given data matrix into a low-rank component and a sparse component containing the outlier elements. The outlier detection using leave-one-out principle might not work in cases where there are many outliers. 3 The initial level of income per capita is a robust and significant variable for growth (in terms of conditional or beta convergence). Rousseeuw PJ, Leroy AM, Robust regression and outlier detection. Robust PCA will be used to detect the atypical genomic markers, which have been . In such cases when the errors are not normal, robust regression is one of the methods that one can use. Structure Discovery in Nonparametric Regression through Compositional Kernel Search - implementation - · Nuit Blanche in Review (May 2013) · How does the Rice one pixel camera work ? Robust Regression and Outlier Detection Average Reviews: (More customer reviews)These authors provide an excellent guide to the available theory of robust regression. To attest that our results were not biased due to statistical outliers, we next performed robust regression analyses using the same explanatory variables.