What are the differences between robust regression and OLS?

What are the differences between robust regression and OLS?

Robust regression is designed to be less sensitive to outliers than Ordinary Least Squares (OLS). OLS minimizes the sum of squared residuals, which can be heavily influenced by outliers, while robust regression uses methods that reduce this influence.

Can you explain how robust regression works?

Sure! Robust regression techniques, like Huber regression, combine the principles of OLS with adjustments to lessen the impact of outliers. They use different loss functions that are less sensitive to extreme values.

When should I use robust regression instead of OLS?

You should consider using robust regression when your data contains outliers or when you suspect that the assumptions of OLS may not hold, such as homoscedasticity.

Are there any specific methods of robust regression I should know about?

Yes, some common methods include Huber regression, RANSAC, and Theil-Sen estimator. Each has its own strengths depending on the data characteristics.

What are the limitations of robust regression?

While robust regression is more resilient to outliers, it can be less efficient than OLS when the data is normally distributed without outliers. It may also require more computational resources.