In this paper, we proposed new conventional unbiased and calibration estimators for estimating finite population variance for a Mail Survey Design characterized by the presence of non-response in practice. The properties of the proposed estimators are studied theoretically and numerically. Empirical studies were conducted using two each of existing and simulated data to illustrate the performance of proposed estimators over existing ones. The results of both theoretical and numerical comparison depicted the superiority of proposed estimators for estimating population variance for a Mail Survey Design characterized by non-response.

The estimation of population coefficient of variation is one of the challenging aspects in sampling survey techniques for the past decades and much effort has been employed to develop estimators to produce its efficient estimate. In this paper, we proposed logarithmic ratio type estimator for the estimating population coefficient of variation using logarithm transformation on the both population and sample variances of the auxiliary character. The expression for the mean squared error (MSE) of the proposed estimator has been derived using Taylor series first order approximation approach. Efficiency conditions of the proposed estimator over other estimators in the study has also been derived. The empirical study was conducted using two-sets of populations and the results showed that the proposed estimator is more efficient. This result implies that, the estimate of proposed estimator will be closer to the true parameter than the estimates of other estimators in the study.

Surveys such as medical, social science, etc conducted by human are often challenged by problem of non-response or missing observations. Different imputation strategies have been developed by several authors to take care of missing observation during analyses. Nevertheless, the estimators involved in some of these schemes depend on unknown parameters of the variable of interest which makes them impractical in real life. In this study, new classes of regression-type compromised imputation method which are free of unknown parameters have been presented. The properties (bias and MSE) of the class of estimators presented were derived up to first order approximation using Taylor series approach. Also, conditions for which the new estimators more efficient than other estimators considered in the study were also established. Numerical examples were conducted and the results revealed that the proposed class of estimators is more efficient.

In this paper, we have proposed three classes of almost unbiased estimators for population mean under simultaneous presence of measurement and non-response error. Asymptotic properties such as Bias and MSE for the proposed classes of estimators are obtained. Numerical illustration in support of theoretical results is also given on two real data sets and a simulated data set using R. Results indicate the superiority of proposed classes of estimators over existing estimators.

In this paper, we have proposed a regression-type compromised imputation methods free of unknown parameters. The properties (biases and MSEs) of the proposed class of estimators are derived up to first order approximation using Taylor series approach. Also, the conditions for which the proposed estimators are more efficient than other estimators considered in the study were established. Results of numerical illustration using both real and simulated data revealed that the proposed estimators are more efficient and practicable than exiting estimators considered in the study.

This paper introduced new classes of calibrated randomized response techniques (C-ORRT) models aimed at estimating sensitive information. The proposed C-ORRT models were developed by modifying existing RRT models using auxiliary variable through calibration methods. The goal was to develop RRT models that are more efficient, stable, and robust than the current options. The theoretical properties such as estimators, variances, privacy levels, and a combined metric for efficiency and privacy to assess the robustness and applicability of the proposed models were derived. The theoretical efficiency conditions of the C-ORRT models were established in comparison to some existing RRT models. Numerical applications using both real and simulated data supported the theoretical findings, demonstrating that the C-ORRT models exhibited lower biases, reduced variances, higher relative efficiency, enhanced privacy levels, and a better combined metric of variance and privacy. This indicates the superiority of the C-ORRT models over existing RRT models.