a novel improved class of ratio-product type exponential estimators of population variance

Several auxiliary information-based estimators of the population variance are available in the existing literature of survey sampling. mostly, these estimators are based on conventional dispersion measures of the auxiliary variable. in this study, a generalized class of ratio-product type exponential estimators of the population variance is proposed which integrates the auxiliary information on non-conventional dispersion measures under simple random sampling in the ratio-type exponential class of estimators. the performance of the proposed estimators is compared, theoretically and numerically, with the several existing estimators of the population variance. it is established that the proposed class of estimators outperforms the existing estimators in terms of the lower mean square and relative root mean square errors. moreover, the percentage relative efficiency of the proposed estimators is much higher as compared to their counterparts.