An alternative ratio estimator is proposed for a finite population mean of a study variable Y in simple random sampling using information on the mean of an auxiliary variable X, which is highly correlated with Y. Expressions for the bias and the mean square error of the proposed estimator are obtained. Both analytical and numerical comparisons have shown the proposed alternative estimator to be more efficient than some existing ones. The bias of the proposed estimator is also found to be negligible for all populations considered, indicating that the estimator is as good as the regression estimator and better than the other estimators under consideration.
Synthetic estimators are known to produce estimates of population mean in areas where no sampled data are available, but such estimates are usually highly biased with invalid confidence statements. This paper presents a calibrated synthetic estimator of the population mean which addresses these problematic issues. Two known special cases of this estimator were obtained in the form of combined ratio and combined regression synthetic estimators, using selected tuning parameters under stratified sampling. In result, their biases and variance estimators were derived. The empirical demonstration of the usage involving the proposed calibrated estimators shows that they provide better estimates of the population mean than the existing estimators discussed in this study. In particular, the estimators were examined through simulation under three distributional assumptions, namely the normal, gamma and exponential distributions. The results show that they provide estimates of the mean displaying less relative bias and greater efficiency. Moreover, they prove more consistent than the existing classical synthetic estimator. The further evaluation carried out using the coefficient of variation provides additional confirmation of the calibrated estimator's advantage over the existing ones in relation to small area estimation.
In this paper, an alternative class of estimators in probability proportional to size (pps) with replacement sampling scheme for multi-character surveys in which the study variables are poorly correlated with selection probabilities is developed. This is achieved by redefining the selection probabilities. Some existing estimators have been shown to be special cases of the proposed class. Numerical illustrations show that some transformations from the proposed class are more efficient than existing estimators under a super-population model.
This study proposes some ratio estimators of the population mean under simple random sampling schemes, in order to tackle the problem of low efficiencies of some existing estimators. An improved exponential ratio estimator of the population mean under simple random sampling scheme and its bias and mean square error have been derived. Further propositions of a generalized form of the exponential ratio estimator of the population mean under simple random sampling scheme has also been made. The Bias and Mean Square Errors of these class of estimators have also been obtained. It is observed that some existing estimators are members of this class of estimators of population mean. Analytical and numerical results indicate that, the Asymptotic Optimal Estimator (AOE) of these proposed estimators of population mean using single auxiliary variable have been found to exhibit greater gains in efficiencies than the classical regression estimators and other existing estimators in simple random sampling scheme.
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