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- MakaleYalnız künye2026A logarithmic-type estimator for finite population variance and its efficiency properties
Iftikhar, Soofia; Alamgir; Zaman, Tolga
This study proposes a novel logarithmic-type estimator for the finite population variance. We derive the mean square error (MSE) expressions, approximated to the first order, along with the associated efficiency conditions for the proposed estimator, as well as for several existing estimators from the literature. A comprehensive simulation study is conducted to compare the efficiency of the new estimator against these established methods. Based on the analysis of a real dataset, the results demonstrate that the proposed estimator achieves the minimum mean square error and satisfies all relevant efficiency criteria. Consequently, it is concluded that the proposed estimator outperforms the other considered estimators in terms of efficiency.
- MakaleAçık Erişim2025A new class of robust ratio estimators for finite population variance
Zaman, Tolga; Bulut, Hasan
It is a general practice to use robust estimates to improve ratio estimators using functions of the parameters of an auxiliary variable. In this study, a new class of robust estimators based upon the Minimum Covariance Determinant (MCD) and the Minimum Volume Ellipsoid (MVE) robust covariance estimates have been suggested for estimating population variance in the presence of outlier values in the data set for the simple random sampling. The expression for the Mean Square Error (MSE) of the proposed class of estimators is derived from the first degree of approximation. The efficiency of the proposed class of robust estimators is compared with some competing estimators discussed in the literature, and found that proposed estimators are better than other mentioned estimators here. In addition, real data set and simulation studies are performed to present the efficiencies of the estimators. We demonstrate theoretically and numerically that the proposed class of estimators performs better than all other competitor estimators under all situations.
- MakaleAçık Erişim2024Improved estimation of population variance in stratified successive sampling using calibrated weights under non-response
Pandey, M. K.; Singh, G. N.; Zaman, Tolga; Al Mutairi, Aned; Mustafa, Manahil SidAhmed
This paper introduces a new method to estimate the population variance of a study variable in stratified successive sampling over two occasions, while accounting for random non -response. The method uses a logarithmic type estimator and leverages information from a highly positively correlated auxiliary variable. The paper also presents calibrated weights for the new estimator and examines its properties through numerical and simulation studies. The results indicate that the suggested estimator is more effective than the standard estimator for estimating the population variance.