Comparative Analysis of the Efficiency of Sampling Scheme Estimators in Estimating Population Total
Article Sidebar
Main Article Content
Sampling is a fundamental tool in statistical research, providing a practical alternative to complete enumeration where of time, cost, personal and accessibility are constraints Choosing the best estimator for population total estimation is one of the main issues in survey sampling. Even though many different sampling strategies and estimators are available, it is still difficult to assess how effective they are in diverse situations. This study presents a comparative analysis of the efficiency of sampling scheme estimators (Hansen Hurwitz, Horvitz-Thompson, Rao-Hartley-Cochran’s and Sen Yates-Grundy) in estimating population total using child birth data) from Ekiti State . Population totals and variances of each estimator were obtained and the most efficient estimator determined in terms of variance. The results revealed that the Rao–Hartley–Cochran estimator consistently produced the lowest population total estimates with the least variance for 2 years, while in the other year, the Sen–Yates–Grundy estimator demonstrated superior efficiency with minimal variance. (The study offers empirical evidence regarding the relative efficiency of the probability proportional to size estimators with replacement (Hansen–Hurwitz) and those without (Horvitz–Thompson, Rao–Hartley–Cochran, and Sen–Yates–Grundy in estimating population totals). The study further showed that' efficiency of estimators is not constant but rather fluctuates over time and across data distributions. The study also closes the gap between theoretical sampling principles and real-world application in demographic and health statistics by using these sample strategies on actual child birth registration data from Ekiti State.
Downloads
References
Anderson, M., and Scott, D. (2024). Investigating the Rao-Hartley-Cochran (RHC) method’s effectiveness in estimating labor force participation rates in informal economies. Journal of Labor Economics, 41(2), 75-88.
Brown, P., Clark, E., and Thompson, J. (2024). The Horvitz-Thompson (HT) estimator’s application in estimating malnutrition prevalence among children in low-income regions. Public Health Statistics Review, 34(2), 56-70.
Cochran, W. G. (1977). Sampling techniques (3rd ed.). Wiley. Chowdhury, S., and Ahmed, N. (2021). Assessment of the Midzuno-Sen sampling scheme in estimating deforestation rates in tropical regions. Environmental Monitoring and Assessment, 172(4), 210-221.
Horvitz, D. G., and Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47(260), 663–685.
Kish, L. (1965). Survey sampling. Wiley.Singh, D., and Mangat, N. S. (1996). Theory and analysis of sample survey designs.
Wiley Yates, F., and Grundy, P. (2018). The Yates-Grundy draw-by-draw method’s application in environmental migration studies. Population and Environment Studies Journal, 30(3), 45
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.