Robust regression and outlier detection epub
Par winston becky le vendredi, décembre 23 2016, 00:51 - Lien permanent
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
Publisher: Wiley
Format: pdf
I encountered a wonderful survey article, "Robust statistics for outlier detection," by Peter Rousseeuw and Mia Hubert. Robust Regression and Outlier Detection (Wiley Series in Probability and Statistics) book download. The outlier detection using leave-one-out principle might not work in cases where there are many outliers. To attest that our results were not biased due to statistical outliers, we next performed robust regression analyses using the same explanatory variables. 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. � Most common regression methods (linear, logistic, etc.) • Time Series Modeling. In such cases when the errors are not normal, robust regression is one of the methods that one can use. Tuesday, 9 April 2013 at 13:07. � Example of embedding graphics from S+/R. � Principal Component Analysis. About robust regression, robust estimators and statistical procedures, outlier detection, extreme value theory, data cleaning, outlier detection in high dimensional data, non parametric statistics.