Improved ratio-type estimators using maximum and minimum values under simple random sampling scheme
| Autor: | Abdullah Y. Al-Hossain, Mursala Khan, Neelam Bashir, Saif Ullah |
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| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Class (set theory) Population mean Estimator General Medicine Type (model theory) Simple random sample Auxiliary variables Scheme (mathematics) Statistics İstatistik ve Olasılık Study variable Auxiliary variable Ratio estimators Maximum and Minimum values Simple random sampling Mean squared error Efficiency Bootstrapping (statistics) Mathematics |
| Zdroj: | Volume: 44, Issue: 4 923-931 Hacettepe Journal of Mathematics and Statistics |
| ISSN: | 1303-5010 2651-477X |
| DOI: | 10.15672/hjms.2014297480 |
| Popis: | This paper presents a class of ratio-type estimators for the evaluation of finite population mean under maximum and minimum values by using knowledge of the auxiliary variable. The properties of the proposed estimators in terms of biases and mean square errors are derived up to first order of approximation. Also, the performance of the proposed class of estimators is shown theoretically and these theoretical conditions are, then, verified numerically by taking three natural populations under which the proposed class of estimators performed better than other competing estimators. 2000 AMS Classification: 62D05. |
| Databáze: | OpenAIRE |
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