期刊名称：Pakistan Journal of Statistics and Operation Research
印刷版ISSN：2220-5810
出版年度：2016
卷号：12
期号：2
页码：353-367
DOI：10.18187/pjsor.v12i2.1182
语种：English
出版社：College of Statistical and Actuarial Sciences
摘要：In recent times, it is common to the make use of auxiliary information to increase the precision of estimators in sample surveys. In this study, we propose some new modified linear regression type ratio estimators for estimating population mean by some non-conventional dispersion measures such as: Gini’s mean difference, Downton’s method and probability weighted moments with linear combination of population correlation coefficient and population coefficient of variation. Expressions for the bias and the mean squared error are derived and are compared with those of the usual ratio estimator and the existing ratio type estimators in literature. Conditions are determined for which the proposed estimators perform better than the existing estimators. Both theoretical and empirical findings show the soundness of the proposed procedure for estimation of population mean.
其他摘要：In recent times, it is common to the make use of auxiliary information to increase the precision of estimators in sample surveys. In this study, we propose some new modified linear regression type ratio estimators for estimating population mean by some non-conventional dispersion measures such as: Gini’s mean difference, Downton’s method and probability weighted moments with linear combination of population correlation coefficient and population coefficient of variation. Expressions for the bias and the mean squared error are derived and are compared with those of the usual ratio estimator and the existing ratio type estimators in literature. Conditions are determined for which the proposed estimators perform better than the existing estimators. Both theoretical and empirical findings show the soundness of the proposed procedure for estimation of population mean.
关键词：Auxiliary variable;Downton’s technique;Gini’s mean difference;Probability weighted moments.
