A Study on the Class of Chain Ratio–type Estimators

 This paper considers the problem of estimating the population mean Ybar of the study variate Y using information on different parameters such as population mean $(bar{X})$, coefficient of variation $(C_x)$, kurtosis  $beta_{2(x)}$, standard deviation $(S_x)$ of the auxiliary variate x and on the correlation coefficient, $rho$, between the study variate $Y$ and the auxiliary variate $x$ through transformation. A class of estimators on the lines of Kadilar and Cingi (2003) has been defined and its properties are studied to the first degree of approximation. It has been shown that the proposed class of estimators is better than usual unbiased estimator $bar{Y}$, ratio estimator $bar{Y}_R$, ratio-type estimator $t_R$ and Kadilar and Cingi (2003) estimator $bar{Y}_{CR}$ under some realistic conditions. Numerical illustration is given in support of the present study.