On Estimation of Finite Population Mean Using Auxiliary Information in Presence of Measurement Errors

Abstract

This study developed a new estimator for estimating population mean in the presence of measurement errors using auxiliary information. Data collection requires careful consideration and security precautions because it is a basic component of all statistical studies. Naturally, at the analysis step, it is assumed that all data recorded were precisely measured. There are a few circumstances, however, in which this assumption of error-free observations might not hold true. It might be possible to acquire the data with few errors. In light of this, a method for estimating the mean of a finite population in the presence of measurement errors is proposed using auxiliary information and approximate first-order equations for bias and mean squared error are also produced. It has been shown through theoretical and numerical studies that the proposed new estimator improves the one already found in the literature.