… an extensive bibliography in robust regression, outlier diagnostics, and related methods.
The aim of this book, the authors tell us, is ‘to make robust regression available for everyday …

… Outlier detection has attracted considerable interest in various areas. Existing outlier detection
… method for outlier detection and robust updating of linear regression problems involving …

… However, in statistical applications of outlier detection and robust regression, the methods
most commonly used today are Huber M estimation, high breakdown value estimation, and …

… robust methods (eg, LMS and LTS) to identify outliers and then applying ordinary least squares
methods when the outliers have … for Robust reGRESSion) that performs LMS regression. …

… this outlier detection using new regression model called Robust Regression Model. Not all
… More of all in this article we are dealing outlier on robust regression on SAS and R using M-…

… to inspect the results of the experiments for outliers: the half-normal plot of the … robust
regression methods are applied to calculate the effects with a minimum influence of possible outliers…

… The earliest theory of robust regression was based on M-estimators,24 R-estimators,25
and L-estimators.26 The breakdown value of all these methods is 0% because of their …

… ) regression can be of great interest in the detection of outlier data … some applications where
this robust regression works in a … RLS regression after deleting the outliers detected by LMS …

… robust discriminative learning has been relatively unexplored in computer vision. This paper
develops the theory of robust regression … to detect intra-sample outliers in robust regression, …

… robust methods allow effective detection of multiple outliers. Furthermore, a combined
approach of robust regression and robust … attempt to move LTS robust regression to a nonlinear …