Robust estimation strategy for handling outliers

ABSTRACT

Classical estimators fail to be efficient in practical scenarios when data is riddled with extreme values known as outliers. Robust estimation strategies are insensitive to outliers and may be used in such cases. The current work is focused on developing a novel robust estimation strategy using Huber M-estimation. A new chain-product type estimator for population mean has been suggested utilizing data on two auxiliary variables. A numerical comparison has been carried out between the proposed robust estimator and the corresponding classical estimator using real and simulated data containing outliers. Recommendations have been made for its practical use based on the encouraging results.

KEYWORDS:

• Survey sampling
• robust regression
• robust estimation
• parameter estimation
• outliers
• Mahalanobis distance

Acknowledgments

The authors are thankful to the anonymous reviewers whose comments helped bring the manuscript to its current state.

Conflicts of interest

The authors have no conflicts of interest to disclose.