Robust regression is an important tool for analyz- ing data that are contaminated with outliers. It can be used to detect outliers and to provide re- sistant (stable) results in the presence of outliers.
In this paper, we make use of robust regression techniques to mitigate the impact of outliers in the training data set. The experiment results show that the ...
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Nov 23, 2017 · A useful tool for this purpose is robust statistics, which aims to detect the outliers by first fitting the majority of the data and then flagging data points ...
Robust Regression and Outlier Detection is a book on robust statistics, particularly focusing on the breakdown point of methods for robust regression.
Missing: anomaly | Show results with:anomaly
Abstract—In our previous work, we have applied ordinary linear regression equation to network anomaly detection. How- ever, the performance of ordinary ...
Jul 31, 2017 · A useful tool for this purpose is robust statistics, which aims to detect the outliers by first fitting the majority of the data and then flagging data points ...
Robust regression is an alternative to least squares regression when data are contaminated with outliers or influential observations.
Robust linear regression is less sensitive to outliers than standard linear regression. Standard linear regression uses ordinary least-squares fitting to ...
Missing: anomaly | Show results with:anomaly
The basic idea of this method is to treat the extreme nonlinear model variability as outliers if they exceed a certain threshold. A bootstrap method for the LTS ...
Jul 20, 2022 · Huber regression is an example of a robust regression algorithm that assigns less weight to observations identified as outliers. To do so, it ...