Almost unbiased estimator using Known Value of Population Parameter (s) in Sample Surveys

TL;DR

This paper introduces an almost unbiased estimator for population parameters in sample surveys, leveraging known auxiliary information to improve estimation accuracy under simple random sampling without replacement.

Contribution

It proposes a new class of estimators that generalize existing methods, incorporating known population parameters to reduce bias and mean square error.

Findings

Derived explicit formulas for bias and MSE of the estimators

Numerical illustrations demonstrate improved estimation performance

Includes and extends previous estimators in the literature

Abstract

In this paper we have proposed an almost unbiased estimator using known value of some population parameter(s). A class of estimators is defined which includes Singh and Solanki [1] and Sahai and Ray [2], Sisodia and Dwivedi [3], Singh et. al. [4], Upadhyaya and Singh [5], Singh and Tailor [6] estimators. Under simple random sampling without replacement (SRSWOR) scheme the expressions for bias and mean square error (MSE) are derived. Numerical illustrations are given in support of the present study. Key words: Auxiliary information, bias, mean square error, unbiased estimator.