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创新的内存类型校准估计器,用于在分层采样中提高调查准确性
Kanwal Shafiq Minhas1, Riffat Jabeen1, Azam Zaka2
1Department of Statistics, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
Scientific reports
|October 3, 2025
概括
使用记忆类型统计数据的新校准方法提高了人口参数估计准确度. 这些先进的估计器始终显示出比传统方法更低的平均平方误差和更高的相对效率.
科学领域:
- 统计 统计 统计 统计
- 调查方法 调查方法
- 数据分析 数据分析
背景情况:
- 校准方法通过整合各种数据源来增强参数估计.
- 记忆类型统计 (EWMA,EEWMA,HEWMA) 使用历史和当前数据来估计人口参数.
研究的目的:
- 在使用内存类型统计数据的校准框架内引入新的比率和产品估计器.
- 评估这些新估计器的性能与现有方法相比.
主要方法:
- 开发新的基于校准的比率和产品估计器,包括EWMA,EEWMA和HEWMA统计数据.
- 进行模拟研究以计算平均平方误差 (MSE) 和相对效率 (RE).
- 通过现实世界的应用程序进行验证.
主要成果:
- 拟议的基于校准的内存类型估计器表现出卓越的性能.
- 与现有的记忆类型估计器相比,观察到持续较低的MSE和更高的RE.
- 图形表示说明了新估计器的强大行为.
结论:
- 基于校准的新型内存类型估计器在准确性和效率方面提供了显著的改进.
- 这些方法为人口参数估计提供了更有效的方法.
- 这些发现证实了提出的估计器对传统技术的优越性.
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