基于Kullback-Leibler差异的引导估计器的概率学对对模型比较
Andres Dajles1, Joseph Cavanaugh1
1Department of Biostatistics, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了用于模型选择的Kullback-Leibler差异 (BD) 的启动近似,为经典假设测试提供了替代方案. 建议对BD估计器进行偏差校正,以提高模型比较的准确性.
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:
- 经典假设测试有其局限性,需要嵌套模型和一个真正的数据生成模型.
- 差异测量为模型选择提供了一个替代方案,没有这些严格的假设.
研究的目的:
- 使用Kullback-Leibler差异 (BD) 的启动近似来进行模型选择.
- 估计一个零模型比另一个替代模型更接近真实数据生成模型的概率.
- 建议和评估BD估计器的偏差纠正.
主要方法:
- 库尔巴克-莱布勒差异的引导式近似.
- 偏差校正方法:基于引导的校正和添加参数的数量.
- 在各种模型比较场景中探索估计器行为.
主要成果:
- 引导式近似 (BD) 提供了一种方法来估计模型差异概率.
- 建议的偏差校正提高了BD估计器的准确性.
- 校正的有效性在不同的模型比较设置中得到证明.
结论:
- 修正后的BD估计器为模型选择提供了一个强大的方法,克服了经典假设测试的局限性.
- 这种方法为研究人员在比较非嵌套模型或当真正的数据生成模型未知时提供了有价值的工具.
- 进一步探索偏差校正技术可以完善模型比较方法.
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