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- Title
A NEW EXPONENTIAL TYPE RATIO ESTIMATOR FOR THE POPULATION MEAN IN SYSTEMATIC SAMPLING.
- Authors
AL e'damat, Ayed; Rather, Khalid Ul Islam
- Abstract
Utilizing auxiliary information effectively in sample surveys can enhance the accuracy of estimations by capitalizing on the relationship between the main variable under study and the auxiliary variable. Estimators such as ratio, product, exponential, and regression estimators are frequently employed either during the estimation process, the design phase, or both. In everyday situations, it is common to incorporate information from one or two auxiliary variables to improve the precision of estimators. Auxiliary information has been in practice in sampling theory since the advent of modern sample surveys. Information on auxiliary variable having high correlation with the variable under study is quite useful in improving the sampling design. Cochran (1940) used the highly positively correlated study and auxiliary variable to propound the ratio estimator. Product estimator requires a high negative correlation between study and auxiliary variable. By reviewing the literature, it is concluded that applying the auxiliary information enhances the efficiencies of the estimators for estimating any parameter under consideration. So it is well established fact that the use of auxiliary variable technique improves the estimation process for target population. It is also noticed that ratio method of estimation is relatively simple and one of the commonly used methods of estimation. Due to limitations in terms of time and cost, sample surveys are often preferred over census surveys for collecting primary data. In these sample surveys, the ratio, product, and regression estimators are frequently employed to estimate the mean or other parameters of interest for the variable under study. To assess their efficiency, these estimators are compared based on their approximate mean squared errors. In this paper we proposed an exponential ratio type estimator for the estimation of finite population mean under systematic sampling. The mean square error of the proposed estimator is computed up to the first order of approximation and we find proposed estimator is efficient as compared to other existing estimators. Furthermore this theoretical result is supported by numerical examples as well.
- Subjects
SAMPLING (Process); MEAN square algorithms
- Publication
Reliability: Theory & Applications, 2023, Vol 18, Issue 3, p442
- ISSN
1932-2321
- Publication type
Article