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

After an For example: neural networks, SVM, rule-based, clustering, nearest neighbors, regression, etc. In fitting regression line outliers can significantly change the slope. They define outlier detection as the problem of “[] finding patterns in data that do not conform to expected normal behavior“. Some statistics are more robust than others to data contamination. An adjusted boxplot rule, based on the upper and lower quartiles, along with a robust skewness estimator called the medcouple. This will mean that such an outlier is caused by an error and is not a true score that can be easily re-measured to get exactly the same result. 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. Properties of estimators and inference. Outliers: detection and robust estimation (RLM) Part 3: Outlook. 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. The first one, Outlier Detection: A Survey, is written by Chandola, Banerjee and Kumar. However an outlier Robust Regression and Outlier Detection. I encountered a wonderful survey article, "Robust statistics for outlier detection," by Peter Rousseeuw and Mia Hubert. We further extend the sparse regression algorithm to a robust sparse regression algorithm for outlier detection, which provides superior accuracy compared to the traditional IQR method. 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. Another useful survey article is “Robust statistics for outlier detection,” by Peter Rousseeuw and Mia Hubert. Brief show case: quantile regression, non-parametric estimation The future of statistics in python. 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. 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.