Open AccessEfficient transformed ratio-type estimator using single auxiliary information
Author(s) -
Muhammad Ismail,
Sana Amjad
Publication year - 2021
Publication title -
maǧallaẗ al-kuwayt li-l-ʿulūm
Language(s) - English
Resource type - Journals
eISSN - 2307-4116
pISSN - 2307-4108
DOI - 10.48129/kjs.v48i2.9030
Subject(s) - estimator , mean squared error , minimum variance unbiased estimator , efficient estimator , bias of an estimator , mathematics , statistics , ratio estimator , invariant estimator , stein's unbiased risk estimate , population variance , consistent estimator , efficiency , variance (accounting) , trimmed estimator , variable (mathematics) , mathematical analysis , accounting , business
This paper provides an efficient transformed ratio-type estimator to estimate the study variable's population variance by utilizing information of a single auxiliary variable under simple random sampling without replacement. The bias and mean squared error of the proposed estimator are derived up-to 1st order approximation. In addition to this, the efficiency comparison of the proposed estimator has been done with traditional ratio-type variance estimator and some other widely used modified ratio-type variance estimators by taking real-life data. A simulation study has also been carried out to see the performance of the proposed estimator. It is worth noticing that our proposed estimator performs better than the competing estimators in real-life data applications as the mean squared error and root mean squared error of our proposed estimator are smaller than the competing estimators. Hence, our proposed estimator is better than existing variance estimators.