In this study, we propose generalized exponential type ratio-cum-ratio estimators of finite population mean using ranked Set Sampling (RSS) and stratified ranked set sampling (SRSS) schemes. The biases and Mean Square Errors (MSEs) of the proposed estimators are obtained up to first degree of approximation. Comparisons among the proposed and some existing estimators are made both theoretically and through simulation studies. Also, the estimators are compared in terms of relative bias (RB), relative mean square error (RRMSE) and percentage relative efficiency (PRE). It turned out that when the variable of interest and the auxiliary variables jointly follow a trivariate normal distribution, the proposed class of estimators of the population mean dominates all other competitor estimators.

This paper addresses new exponential estimators for population mean in case of non-response on both the study and the concomitant variables using simple random sampling. The expressions for theoretical bias and mean square error of new estimators are derived up to first-order approximation and comparisons are made with the existing estimators. The proposed estimators are observed more efficient as compared to the considered estimators in the literature. For instance, the classical [4] unbiased estimator, the estimator of [9], and other existing estimators under the explained conditions. The theoretical results are supported numerically by using real-life data sets, under the criteria of bias, mean square error, percent relative efficiency and mathematical conditions. It is also clear from the numerical results that the suggested exponential estimators performed better than the estimators in the literature.

In this article we propose improved ratio-type estimators for estimating the finite population mean under stratified double-ranked set sampling (S_tDRSS) using the auxiliary information. The biases and mean squared errors (MSE) of the proposed ratio-type estimators are derived up to first order of approximation. The proposed estimators are compared with some competitor estimators both theoretically and numerically. It is identified through numerical and simulation studies that the proposed ratio-type estimators based on S_tDRSS are more efficient than the corresponding estimators in stratified ranked set sampling (S_tRSS) given by Mandowara and Mehta.

In this article, we suggest an efficient class of difference-in-exponential ratio-type estimators having two concomitant variables under ranked set sampling (RSS) design. The bias and mean squared error (MSE) of the proposed estimators are obtained upto first degree of approximation. Comparison among the proposed and competitor estimators are made both theoretically and through simulation study. It turned out that when the variable of interest and the concomitant variables jointly follow a trivariate normal distributions, the proposed class of estimators of the population mean dominates all other competitor estimators. In addition, we support the result with a real data set.