Singh Chandraketu, Kamal Mustafa, Singh Garib Nath, Kim Jong-Min
Department of Mathematics & Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India.
Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Dammam, Kingdom of Saudi Arabia.
Risk Manag Healthc Policy. 2021 Apr 16;14:1595-1613. doi: 10.2147/RMHP.S294731. eCollection 2021.
In biometric sample surveys, our objective is to get ready-made information for future planning and policy implementations related to the subject matters of highly sensitive issues. In such situations, we apply randomized response/scrambled response techniques. There are many highly sensitive issues which need to be examined over time as they may have a tendency to change. To get rid of these types of practical cases we need a scrambled response technique on successive occasions.
Using an additive and multiplicative technique, we proposed new effective scrambled response models to estimate the population mean of quantitative sensitive characteristics. Degree of privacy protection and unified measure approaches are used to examine the efficacy of the proposed models. Efficiency of the proposed models has been checked using MATLAB software. The utility of the proposed models under two occasions of successive sampling has been also explored using exponential-type estimators. Empirical and simulation studies are carried out to justify the proposition of the proposed estimators using MATLAB software.
The percent relative efficiencies of the proposed models are always greater than 100 with respect to the well-known Bar-Lev et al model. In terms of degree of privacy protection, most of the values are greater than 0.5 and closer to 1. Similarly, the values of the proposed models are smaller with respect to the Bar-Lev et al model in terms of a unified measure approach. When the proposed scrambled response models are used on successive occasions, the percent relative efficiency is always found greater than 100 for all cases over its competitors.
In this study, after deeply examining the properties of the proposed models, we found that the proposed models performed better over the well-known existing model. The proposed models may be used in human survey when we deal with highly sensitive issues. The proposed models also performed better when we utilized them in successive sampling. Hence, if sensitive characteristics change with time, the proposed estimators may be the best alternative to deal with these types of situations.
62D05.
在生物特征样本调查中，我们的目标是获取与高度敏感问题相关的现成信息，以便用于未来规划和政策实施。在这种情况下，我们应用随机化回答/加扰回答技术。有许多高度敏感的问题需要随着时间推移进行研究，因为它们可能会发生变化。为了处理这类实际情况，我们需要一种在连续场合下的加扰回答技术。
使用加法和乘法技术，我们提出了新的有效加扰回答模型，以估计定量敏感特征的总体均值。采用隐私保护程度和统一度量方法来检验所提出模型的有效性。使用MATLAB软件检查所提出模型的效率。还使用指数型估计量探讨了所提出模型在连续抽样的两种情况下的效用。使用MATLAB软件进行实证和模拟研究，以证明所提出估计量的合理性。
相对于著名的Bar-Lev等人的模型，所提出模型的相对效率百分比总是大于100。在隐私保护程度方面，大多数值大于0.5且更接近1。同样，在所提出模型在统一度量方法方面的值相对于Bar-Lev等人的模型更小。当在连续场合使用所提出的加扰回答模型时，对于所有情况，相对于其竞争对手，相对效率百分比总是大于100。
在本研究中，在深入研究了所提出模型的性质后，我们发现所提出的模型比著名的现有模型表现更好。当我们处理高度敏感问题时，所提出的模型可用于人类调查。当我们在连续抽样中使用所提出的模型时，它们也表现得更好。因此，如果敏感特征随时间变化，所提出的估计量可能是处理这类情况的最佳选择。
62D05
