神经网络估计器的非对称性属性
Xiaoxi Shen1, Chang Jiang2, Lyudamila Sakhanenko3
1Texas State University, San Marcos, TX, USA.
概括
在AI中广泛使用的神经网络,使用非参数回归和估计进行分析. 这种方法通过模拟验证了神经网络估计器的一致性,收率和非对称正常性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 统计学理论统计学理论
背景情况:
- 神经网络是流行的AI和机器学习工具.
- 全球近似定理指出神经网络可以近似连续函数.
- 神经网络的参数分析由于参数无法识别而具有挑战性.
研究的目的:
- 在非参数回归框架内分析神经网络估计器.
- 建立神经网络估计的理论属性.
- 为了应对导出神经网络的非对称性质的挑战.
主要方法:
- 使用子估计技术.
- 应用非参数回归框架.
- 导出一致性,收率和非对称的正常性.
主要成果:
- 建立了神经网络估计器的一致性.
- 确定了这些估计器的收率.
- 证明了神经网络估计器的非对称正常性.
- 通过模拟研究验证了理论发现.
结论:
- 使用估计的非参数方法为分析神经网络提供了一个强大的框架.
- 对于神经网络估计器来说,已经建立了如一致性和非对称性正常性等理论性质.
- 模拟证实了开发的统计理论的实际有效性.
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