通过辅助和先前信息来帮助著名的库克的随机响应技术
Zawar Hussain1, Ishtiaq Hussain2, Salman A Cheema3
1Department of Statistics, Faculty of Computing, The Islamia University of Bahawalpur, Pakistan.
Heliyon
|March 25, 2024
概括
随机响应技术 (RRT) 提高了对敏感主题的真实数据收集. 本研究引入了使用辅助信息的增强混合估计器,大大提高了调查的准确性.
科学领域:
- 调查方法 调查方法
- 统计推理 统计推理
- 数据收集技术 数据收集技术
背景情况:
- 在调查中直接询问敏感特征对数据准确性构成挑战.
- 随机响应技术 (RRT) 是一种广泛采用的间接询问方法,以减轻这些挑战.
- RRT应用于各种领域,包括行为科学,流行病学和犯罪学.
研究的目的:
- 探索使用辅助信息增强随机响应技术 (RRT) 的方法.
- 在现有RRT模型的基础上提出更高效的混合估计器.
- 提高调查数据中估计敏感比例的精度和准确性.
主要方法:
- 开发结合历史记录或专家意见的辅助信息的混合估计器.
- 当辅助信息可用时,将回归与比率估计用于Kuk (1990) RRT模型.
- 使用Thompson (1968) 的收缩估计,以提高Kuk (1990) RRT模型的精度.
主要成果:
- 拟议的混合估计器与现有方法相比,显示出更高的效率.
- 回归与比率和收缩估计策略显著提高了RRT的准确性.
- 数字调查证实了拟议方法的实质性性能改进.
结论:
- 辅助信息的整合提供了一个强大的战略,以补贴和改进RRT.
- 拟议的混合估计器提供了更精确和准确的敏感比例估计.
- 这项研究强调了将补充数据纳入 robust 调查估计的价值.
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