A New Generalized Class of Exponential Factor-Type Estimators for Population Distribution Function Using Two Auxiliary Variables

Abstract

In this article, we propose a generalized class of exponential factor type estimators for estimation of the finite population distribution function (PDF) using an auxiliary variable in the form of the mean and rank of the auxiliary variable exist. The expressions of the bias and mean square error of the estimators are computed up to the first order approximation. The proposed estimators provide minimum mean square error as compared to all other considered estimators. Three real data sets are used to check the performance of the proposed estimators. Moreover, simulation studies are also carried out to observe the performances of the proposed estimators. The proposed estimators confirmed their superiority numerically as well as theoretically by producing efficient results as compared to all other competing estimators.

Improved family of estimators for the population mean using supplementary variables under PPS sampling

“…Enchanting inspiration from Ahmad et al, 23 we suggested a family of estimators that includes many more effective estimators expending two supplementary variables. The main advantages of our suggested estimator is that it is further elastic, effective than the existing estimators, which is given by:…”

“…An appropriate usage of the supplementary variables may help in improving the exactness of an estimator both during the design stage and at the estimation stage. Enchanting inspiration from Ahmad et al, 23 we suggested a family of estimators that includes many more effective estimators expending two supplementary variables. The main advantages of our suggested estimator is that it is further elastic, effective than the existing estimators, which is given by: where …”

A simulation study: An enhanced generalized class of estimators for estimation of population proportion using twofold auxiliary attribute

“…In such circumstances, estimators for population constraints can be created expending preceding knowledge of the auxiliary characteristics and the relationship amongst the study attribute and the auxiliary attribute. Several important studies, such as [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , 16 , 17 , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] ] have recommended various estimators for the estimation of the exhausting auxiliary attribute of the determinate population proportion.…”

Improved Estimation of Finite Population Variance Using Dual Supplementary Information under Stratified Random Sampling

“…Stratifcation increases precision when the variance among the strata is much larger than the variances within the strata. Some important references under stratifed random sampling are Rao and Shao [14], Kadilar and Cingi [15,16], Singh and Vishwakarma [17], Koyuncu and Kadilar [18], Shabbir and Gupta [19], Ozel et al [20], Sidelel et al [21], Ahmad and Shabbir [22], Hussain et al [23], Shehzad et al [24], Singh et al [25], Zaman [26], Zaman and Bulut [27], and Ahmad et al [28].…”