Optimized inferences of finite population mean using robust parameters in systematic sampling

In this article, we have proposed a generalized estimator for mean estimation by combining the ratio and regression methods of estimation in the presence of auxiliary information using systematic sampling. We incorporated some robust parameters of the auxiliary variable to obtain precise estimates o...

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Bibliographic Details
Published in:PloS one 2023-01, Vol.18 (1), p.e0278619-e0278619
Main Authors: Shaheen, Nazia, Qureshi, Muhammad Nouman, Alamri, Osama Abdulaziz, Hanif, Muhammad
Format: Article
Language:English
Subjects:
Analysis
Bias
Biology and Life Sciences
Computer Simulation
Estimation theory
Estimators
Expressions (Mathematics)
Finite element method
Land surveys
Mathematical analysis
Methods
Models, Statistical
Parameter estimation
Parameter robustness
Physical Sciences
Population biology
Regression Analysis
Research and Analysis Methods
Robustness (mathematics)
Sampling
Surveys and Questionnaires
Variables
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Summary:In this article, we have proposed a generalized estimator for mean estimation by combining the ratio and regression methods of estimation in the presence of auxiliary information using systematic sampling. We incorporated some robust parameters of the auxiliary variable to obtain precise estimates of the proposed estimator. The mathematical expressions for bias and mean square error of proposed the estimator are derived under large sample approximation. Many other generalized ratio and product-type estimators are obtained from the proposed estimator using different choices of scalar constants. Some special cases are also discussed in which the proposed generalized estimator reduces to the usual mean, classical ratio, product, and regression type estimators. Mathematical conditions are obtained for which the proposed estimator will perform more precisely than the challenging estimators mentioned in this article. The efficiency of the proposed estimator is evaluated using four populations. Results showed that the proposed estimator is efficient and useful for survey sampling in comparison to the other existing estimators.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0278619