Robust estimation in finite population sampling

TL;DR

This paper introduces robust estimators for the finite population mean that effectively handle outliers, including robust versions of common estimators like the ratio and Horvitz-Thompson, based on predictive influence functions.

Contribution

It presents a new class of robust estimators for finite population means, extending traditional estimators with improved outlier resistance.

Findings

Robust estimators outperform classical ones in the presence of outliers.

The proposed estimators include robust ratio and Horvitz-Thompson estimators.

Derived using predictive influence functions for enhanced robustness.

Abstract

The paper proposes some robust estimators of the finite population mean. Such estimators are particularly suitable in the presence of some outlying observations. Included as special cases of our general result are robust versions of the ratio estimator and the Horvitz-Thompson estimator. The robust estimators are derived on the basis of certain predictive influence functions.