奥斯班德的识别功能原理
Timo Dimitriadis1,2, Tobias Fissler3,4, Johanna Ziegel5
1Alfred Weber Institute of Economics, Heidelberg University, Bergheimer Str. 58, 69115 Heidelberg, Germany.
概括
这项研究描述了识别功能,这些功能对于统计估计和预测验证至关重要. 我们为各种统计函数定义了这些函数,这些函数在真实值时的预期为零.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 机器学习 机器学习
背景情况:
- 在统计推断中,识别功能是基本的.
- 它们对于验证预测和动态模型至关重要.
- 现有的文献缺乏向量值函数的完整表征.
研究的目的:
- 为了充分描述严格识别函数的类别.
- 将识别函数的理解扩展到向量值的函数.
- 为它们在统计建模中的应用提供一个严格的框架.
主要方法:
- 数学推导和理论分析.
- 在温和的规律条件下对属性的探索.
- 识别函数空间的表征.
主要成果:
- 提供了严格识别功能的完整描述.
- 该理论被扩展到处理向量值的统计函数.
- 函数的衍生类满足了关键的理论性质.
结论:
- 该研究提供了对识别函数的全面理解.
- 这项工作促进了统计估计和预测验证方面的进步.
- 这些发现适用于复杂的,向量值的函数估计问题.
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