Shabbir Javid, Movaheedi Zabihullah
Department of Statistics, University of Wah, Wah Cantt, Islamabad, Pakistan.
Faculty of Science, Department of Mathematics, Herat University, Herat, Afghanistan.
PLoS One. 2024 Dec 20;19(12):e0315658. doi: 10.1371/journal.pone.0315658. eCollection 2024.
Gupta et al. suggested an improved estimator by using the Diana and Perri model in estimating the finite population variance using the single auxiliary variable. On the same lines, Saleem et al. proposed a new scrambled randomized response model (RRT) based on two auxiliary variables for estimating the finite population variance. Recently Azeem et al. presented a new randomized response model in estimating the finite population variance. It is observed that Bias and MSE of these estimators up to first order of approximation seem to lack sufficient information. In this study, we rectify the bias and MSE expressions of the estimators proposed by Gupta et al., Saleem et al. and Azeem et al. Additionally, we suggest a new generalized class of estimators that is more efficient in comparison to the previously considered estimators. A simulation study is conducted to establish the behavior of the estimators. The suggested estimator performs better than the estimators considered by the authors earlier.
古普塔等人建议在使用单个辅助变量估计有限总体方差时,通过使用戴安娜和佩里模型来改进估计量。同样,萨利姆等人基于两个辅助变量提出了一种新的加扰随机响应模型(RRT)来估计有限总体方差。最近,阿齐姆等人提出了一种用于估计有限总体方差的新随机响应模型。据观察,这些估计量在一阶近似下的偏差和均方误差似乎缺乏足够的信息。在本研究中,我们纠正了古普塔等人、萨利姆等人和阿齐姆等人提出的估计量的偏差和均方误差表达式。此外,我们建议了一类新的广义估计量,与之前考虑的估计量相比,它更有效。进行了一项模拟研究以确定这些估计量的性能。所建议的估计量比作者之前考虑的估计量表现更好。