What are the limitations of robust regression?

What are the limitations of robust regression?

Robust regression is designed to handle outliers, but it has some limitations. For instance, it can be less efficient than ordinary least squares when the data is normally distributed.

Can you explain why it's less efficient in that case?

Certainly! When the data is normally distributed, ordinary least squares provides the best linear unbiased estimates. Robust regression, while more resistant to outliers, may not utilize all the information effectively, leading to less efficient estimates.

Are there specific scenarios where robust regression is preferred?

Yes, robust regression is preferred in scenarios with significant outliers or when the underlying data distribution is unknown. It helps in obtaining more reliable estimates in such cases.

What are some common robust regression techniques?

Common techniques include Huber regression, Least Absolute Deviations (LAD), and RANSAC. Each has its own approach to handling outliers.