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Robust regression and outlier detection book
Robust regression and outlier detection book

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

Robust regression and outlier detection


Robust.regression.and.outlier.detection.pdf
ISBN: 0471852333,9780471852339 | 347 pages | 9 Mb


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Robust regression and outlier detection Annick M. Leroy, Peter J. Rousseeuw
Publisher: Wiley




(2003), The Impact of Trade on Intra-Industry Reallocations and. 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. Robust Nonlinear Fitting by RANSAC (Outlier Tolerent Nonlinear Regression). While this rule is appropriate for symmetric, approximately Gaussian data distributions, highly asymmetric situations call for an outlier detection rule that treats upward-outliers and downward-outliers differently. About robust regression, robust estimators and statistical procedures, outlier detection, extreme value theory, data cleaning, outlier detection in high dimensional data, non parametric statistics. Robust Nonlinear Optimization MATLAB Code Download Please have a look in RANSAC_4_Nonlinear.m This code is not heavily tested. Leroy (1987), Robust Regression and Outlier. Like covMcd, and robust fitting procedures like lmrob and glmrob for linear models and generalized linear models (specifically, a robust logistic regression procedure for binomial data, and a robust Poisson regression procedure for count data), among others. Aggregate Industry Productivity. 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. Econometrica 71 (6), 1695-1725. There are also methods for linear regression which are resistant to the presence of outliers, which fall into the category of robust regression.

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