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

Robust regression and outlier detection



Download eBook




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


Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). They define outlier detection as the problem of “[] finding patterns in data that do not conform to expected normal behavior“. 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. In fitting regression line outliers can significantly change the slope. I am have been working on a more robust regression boosting algorithm for my undergraduate thesis. Mahwah, NJ: Applied regression analysis (2nd ed.). Another useful survey article is “Robust statistics for outlier detection,” by Peter Rousseeuw and Mia Hubert. I had a discussion the other day about using the weights returned by boosting to do outlier detection. After an For example: neural networks, SVM, rule-based, clustering, nearest neighbors, regression, etc. The first one, Outlier Detection: A Survey, is written by Chandola, Banerjee and Kumar. Milwaukee Robust regression and outlier detection. New York: How to detect and handle outliers.