A general family of dual to ratio-cum-product estimator in sample surveys

TL;DR

This paper introduces a new family of dual to ratio-cum-product estimators for finite population means, demonstrating improved efficiency over existing estimators under simple random sampling without replacement.

Contribution

The paper develops a new family of estimators with derived bias and MSE expressions, showing they outperform several existing estimators in efficiency.

Findings

Proposed estimators have lower MSE than traditional estimators.

The new family is more efficient than existing estimators like Singh and Srivenkataramana.

Empirical results confirm improved performance of the proposed estimators.

Abstract

This paper presents a family of dual to ratio-cum-product estimators for the finite population mean. Under simple random sampling without replacement (SRSWOR) scheme, expressions of the bias and mean-squared error (MSE) up to the first order of approximation are derived. We show that the proposed family is more efficient than usual unbiased estimator, ratio estimator, product estimator, Singh estimator (1967), Srivenkataramana (1980) and Bandyopadhyaya estimator (1980) and Singh et.al. (2005) estimator. An empirical study is carried out to illustrate the performance of the constructed estimator over others.