Many ratio type estimators for population mean have come into play in the past. Researchers over the years have been making efforts to improve the efficiency of thee estimators. There has been a lot of modification of some of these estimators. Some forms of comparison have been done in the literature. There is need to further compare these estimators with other existing estimators at varying sample sizes and also considering discrete and continuous distribution. Thirty-eight estimators, five different sample sizes and seven distributions were considered. The population mean estimates and their Bias were computed for the thirty- eight estimators at varying sample sizes under various distribution. The efficiency of the estimator was computed using Mean Square Error (MSE). Using simulation study, it was observed that the efficiency of the estimators increase as sample sizes increases and the estimator performed alike in most distributions
Efficiency of Ratio Estimators under Maximum and Minimum values using Simple Random Sampling Scheme (Review Completed - Accepted)
This paper presents a class of ratio estimators for the estimation of finite population mean under maximum and minimum values and 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 are shown theoretically and these theoretically conditions are verified by numerically by taking three natural populations under which the proposed class of estimators performed better than the others competing estimators.