一般化的测量误差:内在和偶然的测量误差
1Measurement, Evaluation, and Research Methodology, University of British Columbia, Vancouver, British Columbia, Canada.
PloS one
|June 29, 2023
概括
本研究为随机值数据引入了两种新型的测量误差 (内在误差和偶然误差). 它将经典错误模型和统计理论推广到这个更广泛的测量领域.
科学领域:
- 统计 统计 统计 统计
- 测量理论 测量理论
- 数据科学数据科学数据科学
背景情况:
- 传统的测量误差模型适用于确定性数据.
- 处理随机变量值的数据需要新的方法来处理测量误差.
研究的目的:
- 为了对随机变量值数据的测量误差概念进行概括.
- 引入内在和偶然的测量误差.
- 将经典的统计方法扩展到这个新的数据领域.
主要方法:
- 内在和偶然的测量误差的表述.
- 对一般化错误模型的校准条件的定义.
- 概括点估计,推理和概率理论的探索.
主要成果:
- 内在和偶然的测量误差之间的区别.
- 经典测量误差模型的概括,包括伯克森误差.
- 对随机变量值测量的统计推理的调整.
结论:
- 拟议的框架可以容纳更广泛的测量数据.
- 一般化统计理论为分析复杂的测量过程提供了工具.
- 这项工作促进了对各种应用中的测量误差的理解和建模.
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