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在统计推理中,无损转换和超额风险边界在统计推理中
László Györfi1, Tamás Linder2, Harro Walk3
1Department of Computer Science and Information Theory, Budapest University of Technology and Economics, H-1111 Budapest, Hungary.
Entropy (Basel, Switzerland)
|October 28, 2023
概括
本研究引入了统计推理中的超额最小风险,定义了无损转换,并为它们开发了一种一致的测试. 它还为各种应用中的无损变换提供了信息理论界限.
科学领域:
- 统计推理 统计推理
- 信息理论 信息理论
- 机器学习 机器学习
背景情况:
- 超额最小风险量化了统计估计中的信息丢失.
- 了解保存基本信息的转换对于有效推断至关重要.
研究的目的:
- 在统计推理中定义和描述无损转换.
- 开发一种用于识别无损转换的统计测试.
- 为一般损失函数建立过度风险的信息理论边界.
主要方法:
- 超额最小风险和无损转换的定义.
- 构建用于无损假设测试的分区测试统计.
- 导出了信息理论上限的过度风险.
- 介绍了 δ 无损转换的概念.
主要成果:
- 无损转换的表征.
- 对于i.i.d.的分区测试具有强大的一致性. 数据. 数据. 数据.
- 关于过度风险的统一信息理论上限.
- 对于普遍的无损 δ 转换有足够的条件.
结论:
- 识别和测试无损转换,对数据分析产生影响.
- 信息理论界限为评估转化效率提供了一个一般框架.
- 这些概念广泛适用于各种领域,包括深度学习和计量经济学.
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