Novel Log Type Class Of Estimators Under Ranked Set Sampling

Abstract

This paper suggests some novel class of log type estimators for the estimation of population mean of study variable under ranked set sampling by utilizing information on population mean of auxiliary variable. The mean square error of the proposed class of estimators is obtained to the first order of approximation. We have compared the proposed class of estimators with some existing competitors under some specific conditions. The theoretical results are validated by a computational study using real and simulated data sets. On the lines of McIntyre (Aust.

Novel predictive estimators using ranked set sampling

“…These predictive estimators provide a better alternative to the EPEs discussed in the preceding section. In our proposal, motivated by Reference 32, we propose some novel logarithmic predictive estimators of population mean $$$ \overline{Y} $$$ under RSS as $$$$ {\mathrm{\gimel}}_{a{k}_1},\mathrm{rss}\kern0.5em ={\alpha}_1\frac{mr}{N}{\overline{y}}_{\mathrm{rss}}+\left(\frac{N- mr}{N}\right){\alpha}_1{\overline{y}}_{\mathrm{rss}}{\left\{1+\log \left(\frac{{\overline{x}}_{\mathrm{rss}}}{{\overline{X}}_{\vartheta }}\right)\right\}}^{\beta_1}, $$$$ $$$$ {\m...$$…”

New efficient logarithmic estimators using multi‐auxiliary information under ranked set sampling

“…The objective of the present article is to suggest an efficient alternative to the surveyors for the estimation of population mean. In our proposal, we have extended the work of Bhushan and Kumar 29,33 in the case of multi‐auxiliary information under RSS.…”

“…Recently, Khan et al 28 introduced some efficient estimators for the population mean consisting of bivariate auxiliary information. In the present article, we have considered a new logarithmic class of estimator employed by Bhushan and Kumar 29 using multi‐auxiliary information under RSS.…”

Partial stratified ranked set sampling scheme for estimation of population mean and median

“…For more modified schemes of RSS, see Al-Nasser et al [7], Bani-Mustafa et al [8], Samawi [9], Salehi and Ahmadi [10], Majd and Saba [11], Sevinc et al [12], Khan et al [13] and Ali et al [14], Monjed et al [15]. For different efficient classes of estimators under RSS and stratified ranked set sampling (StRSS), see Bhushan et al [16][17][18][19][20][21].…”