Method of estimation in the presence of non-response and measurement errors simultaneously

TL;DR

This paper develops a new class of estimators for finite population means that effectively handle both non-response and measurement errors in survey sampling, improving efficiency over traditional methods.

Contribution

It introduces a novel estimator class that accounts for simultaneous non-response and measurement errors, enhancing estimation accuracy in survey sampling.

Findings

Proposed estimators are more efficient than traditional unbiased, ratio, and product estimators.

Numerical study confirms improved performance of the new estimators.

The method effectively reduces bias and variance in the presence of errors.

Abstract

The present paper discusses the problem of estimating the finite population mean of study variable in simple random sampling in the presence of non response and response error together. The estimators in this article use auxiliary information to improve efficiency and we suppose that non response and measurement error are present in both the study and auxiliary variables. A class of estimators has been proposed and its properties are studied in the simultaneous presence of non-response and response errors. It has been shown that proposed class of estimators is more efficient than the usual unbiased estimator, ratio and product estimators under non-response and response error together. In addition, a numerical study is carried out to compare the performance of the proposed class of estimators over others.