Ratio Estimators in Simple Random Sampling Using Information on Auxiliary Attribute

TL;DR

This paper proposes new ratio estimators for population mean estimation using auxiliary attribute information in simple random sampling, deriving bias and MSE expressions, and illustrating results with numerical examples.

Contribution

It introduces novel ratio estimators leveraging auxiliary attribute data and derives their bias and MSE under SRSWOR, with empirical illustrations.

Findings

New ratio estimators have lower bias and MSE compared to existing methods.

Numerical examples demonstrate improved estimation accuracy.

Theoretical bias and MSE expressions are validated empirically.

Abstract

Some ratio estimators for estimating the population mean of the variable under study, which make use of information regarding the population proportion possessing certain attribute, are proposed. Under simple random sampling without replacement (SRSWOR) scheme, the expressions of bias and mean-squared error (MSE) up to the first order of approximation are derived. The results obtained have been illustrated numerically by taking some empirical population considered in the literature.