How to Make Linear Regression Survive Outliers

Standard linear regression using Ordinary Least Squares (OLS) is highly vulnerable to outliers because its squared-loss function gives disproportionate influence to observations with large errors. A few bad data points can significantly skew the resulting model away from the true underlying trend. To counteract this, robust estimation methods are employed to reduce the impact of these anomalous observations. The content compares classical robust estimators like Huber regression and RANSAC with modern approaches such as GNC-GM, GNC-TLS, and ASOR. These techniques are evaluated on their ability to maintain accuracy and efficiency when data is progressively contaminated with outliers.