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

3 The initial level of income per capita is a robust and significant variable for growth (in terms of conditional or beta convergence). To attest that our results were not biased due to statistical outliers, we next performed robust regression analyses using the same explanatory variables. Parameters of the regression models in the bootstrap procedure. Whole host of other multivariate methods. I think that the Lewandowsky data set may have a chance of entering the robust regression textbooks. Leroy, “Robust regression and outlier detection”, John Wiley &. Robust Regression And Outlier Detection Wiley Series In Probability And Statistics (99.75) Robust Statistics: The Approach Based On Influence Functions. As an alternative, a robust method was put . This program has the ability to identify a certain percentage of outliers in each bootstrap sample. That is the only positive aspect of the Lewandowsky research I've thus far been able to detect. Unfortunately, many statistics practitioners are not aware of the fact that the OLS method can be adversely affected by the existence of outliers. Step 4: Fit the LTS to the bootstrapped values b yi on the fixed X to obtain bˆ b. The supplementary online material for the article is being extended to contain additional information (e.g., the outlier analysis from the preceding post).