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This paper suggests a two-parameter ratio-product-ratio type exponential estimator for a finite population mean in simple random sampling without replacement (SRSWOR) following the methodology in the studies of Singh and Espejo (2003) and Chami et al (2012). The bias and mean squared error of the suggested estimator are obtained to the first degree of approximation. The conditions are obtained in which suggested estimator is more efficient than the sample mean, classical ratio and product estimators, ratiotype and product type exponential estimators. An empirical study is given in support of the present study.

In this paper we have suggested a family of estimators of the population mean using auxiliary information in sample surveys. The bias and mean squared error of the proposed class of estimators have been obtained under large sample approximation. We have derived the conditions for the parameters under which the proposed class of estimators has smaller mean squared error than the sample mean, ratio, product, regression estimator and the two parameter ratio-product-ratio estimators envisaged by Chami et al (2012). An empirical study is carried out to demonstrate the performance of the proposed class of estimators over other existing estimators. Mathematics Subject Classification 2000: 62D05

Classes of ratio-type estimators t (say) and ratio-type exponential estimators te (say) of the population mean are proposed, and their biases and mean squared errors under large sample approximation are presented. It is the class of ratio-type exponential estimators te provides estimators more efficient than the ratio-type estimators.

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