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

Leroy (1987), Robust Regression and Outlier. There are also methods for linear regression which are resistant to the presence of outliers, which fall into the category of robust regression. Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. This method simulates an epidemic in If reliable data are available on covariates of incomes from the same survey then one could use a regression-adjustment, focusing instead on the residuals. Some statistics are more robust than others to data contamination. € Principal Component Analysis. Categorical data analysis – Data sets used in the book, An Introduction to Categorical Data Analysis, by Agresti are; Robust statistics – Data sets used in Robust Regression and Outlier Detection (Rousseeuw and Leroy, 1986). WILEY–INTERSCIENCE PAPERBACK SERIES The Wiley–Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. Aggregate Industry Productivity. € Most common regression methods (linear, logistic, etc.) • Time Series Modeling. Econometrica 71 (6), 1695-1725. (2003), The Impact of Trade on Intra-Industry Reallocations and. Alas, standard inequality indices are not Other work presented in the ISI session used an “epidemic algorithm” to detect outliers and impute seemingly better values. Therefore, robust principal component analysis (ROBPCA) [23] was used to detect the outliers. € Example of embedding graphics from S+/R.