Improved Mixed Estimator Using Two Auxiliary Variables For Full Extreme Maximum And Minimum Values In Single Phase Sampling
Abstract
The use of multiple auxiliary variables has been established to improve precision in the estimators of ratio, regression and product respectively. However, the presence of extreme values in the distribution could annul such efficiency Olatayo et al. (2020). Extreme values could be small or minimum, large or maximum values. This study had developed a ratio-cum-regression estimator with two auxiliary variables, correlation coefficient and coefficient of variation under two types of extreme values in the distribution. This study considers full extreme value cases which assumed that both the study and two auxiliary variables had extreme values present in their distributions. Theoretical, empirical and percentage relative efficiency analyses were carried out for Full High and Maximum Extreme Values (FHMaEV) and Full Low and Minimum Extreme Values cases (FLMiEV). The analysis showed that the developed estimator is efficient over the reviewed estimators.
References
- Agunbiade, D. A. and Ogunyinka, P. I. (2013). Effect of Correction Level on the use of AuxiliaryVariable in Double Sampling for Regression Estimation. Open Journals of Statistics, 3(5): ISSN:2161-718,312-318.
- Al Hossain, A. and Khan, M. (2014). Efficiency of ratio, product and regression estimators under maximum and minimum values using two auxiliary variables, The Scientific World Journal, Article ID: 693783, 1-6.
- Cochran, W. G. (1940). The estimation of the yields of cereal experiments by sampling for the ratio of grain to total produce. The Journal of Agricultural Science, 30(2), 262-275.
- Kadilar, C. and Cingi, H. (2005). A new estimator using two auxiliary variables. Applied Mathematics and Computation, 162 (2): 901908.
- Khan, M. and Shabbir, J. (2013). Some improved ratio, product and regression estimators of finite population mean when using minimum and maximum values. The Scientific World Journal, 013: Article ID 431868, 7 pages.
- Mohanty, S. (1967). Combination of Regression and Ratio Estimate. Journal of Indian Statistical Association, 5, 16-19.
- Ogunyinka, P. I., Emmanuel, F. O. and Sodipo, A. A. (2019). General Class of ratio-cum- product estimators in two- phase sampling using multi-auxiliary variables. Anale.Seria XVII fasc.2
- Ogunyinka, P. I., Olatayo, T. O. and Onibudo, M. M. (2021). Improved Ratio Type Estimators for Extreme Maximum and Minimum Value Cases in Single-Phase Sampling Scheme. Futo Journal Series (FUTOJNLS), 7(1), 263-277.
- Olatayo, T.O., Onibudo, M. M. and Ogunyinka, P. I. (2020). Extended Ratio Type Estimators Using Correlation Coefficient and Coefficient of Variation for Full Extreme Maximum and Minimum Values in Single Phase Sampling. Nigerian Journal of Physics, 29(2), 172-181
- Raj, D. (1965). On a Method of Using Multi-auxiliary Information in Sampling Surveys. J. Statist. Assoc., 60, 270-277.
- Srndal, C. E. (1972). Sample survey theory vs general statistical theory: Estimation of the population mean. International Statistical Review/Revue Internationale de Statistique, 40(1), 1-12
