Efficient Ratio and Product Estimators in Stratified Random Sampling

Abstract

This paper suggests an efficient class of ratio and product estimators for estimating the population mean in stratified random sampling using auxiliary information. It is interesting to mention that, in addition to many, Koyuncu and Kadilar (2009), Kadilar and Cingi (2003, 2005), and Singh and Vishwakarma (2007) estimators are identified as members of the proposed class of estimators. The expressions of bias and mean square error (MSE) of the proposed estimators are derived under large sample approximation in general form. Asymptotically optimum estimator (AOE) in the class is identified alongwith its MSE formula. It has been shown that the proposed class of estimators is more efficient than combined regression estimator and Koyuncu and Kadilar (2009) estimator. Moreover, theoretical findings are supported through a numerical example.

Keywords:

• Combined ratio estimator
• Combined product estimator
• Combined regression estimator
• Efficiency
• Mean squared error
• Stratified random sampling

Acknowledgments

The authors are deeply grateful to the Editor-in-Chief Professor N. Balakrishnan and to the referees for their helpful comments and valued suggestions that led to this improved version of the article.