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

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. 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. 3 The initial level of income per capita is a robust and significant variable for growth (in terms of conditional or beta convergence). Robust Nonlinear Fitting by RANSAC (Outlier Tolerent Nonlinear Regression). € Principal Component Analysis. An adjusted boxplot rule, based on the upper and lower quartiles, along with a robust skewness estimator called the medcouple. € Most common regression methods (linear, logistic, etc.) • Time Series Modeling. Leroy, “Robust regression and outlier detection”, John Wiley &. Robust Regression and Outlier Detection. 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. Robust Nonlinear Optimization MATLAB Code Download Please have a look in RANSAC_4_Nonlinear.m This code is not heavily tested. € Example of embedding graphics from S+/R.