What are the main challenges in robust statistics?

What are the main challenges in robust statistics?

Robust statistics deals with data that may contain outliers or deviations from assumptions. Key challenges include identifying outliers, ensuring model stability, and balancing sensitivity and robustness.

How do outliers affect statistical analysis?

Outliers can skew results, leading to misleading conclusions. They can affect measures like the mean and variance, making robust methods essential.

What methods are used to handle outliers?

Common methods include trimming, winsorizing, and using robust estimators like the median or M-estimators that reduce the influence of outliers.

Can you give an example of a robust statistical method?

Sure! The median is a robust measure of central tendency, as it is less affected by extreme values compared to the mean.

What are the limitations of robust statistics?

Limitations include potential loss of efficiency with large datasets and the complexity of selecting appropriate robust methods for different scenarios.