Estimation of coefficient of quartile deviation in case of missing data

Abstract

Ambati, Sedory, and Singh (2018) proposed an estimator for estimating the population coefficient of quartile deviation in the presence of complete information. In this note, focusing on the same goal, we have considered the problem of missing data. We have proposed one ratio and three regression type estimators by introducing a new adjustment technique of missing information. We obtain the expressions of bias and mean square error of proposed estimators under large sample approximation, and compare the results via the efficiency comparisons. The merits of proposed estimators are investigated under a numerical study consisting of four real data sets. The efficient performances of the proposed estimators have also been discussed through a simulation study.

Keywords:

• Population coefficient of quartile deviation
• auxiliary information
• efficiency
• missing data

AMS SUBJECT CLASSIFICATION:

• 62D05

Acknowledgements

Authors are highly thankful to the Indian Institute of Technology (Indian School of Mines), Dhanbad for providing financial assistance and necessary infrastructure to accomplish the present research work.