使用受限分数测试对函数值参数的推理
Aaron Hudson1, Marco Carone2, Ali Shojaie2
1Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
概括
本研究引入了一个新的框架,用于统计推断复杂的函数,如回归和密度,使用一个非参数的得分测试扩展. 该方法为数据分析中具有挑战性的估计问题提供了一种通用方法.
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:
- 在非参数和半参数模型中,对局部参数 (例如密度,回归函数) 的推断至关重要,但具有挑战性.
- 这些复杂的估计值通常很难以参数速率进行估计,这阻碍了校准推断.
- 许多这样的估计值可以表示为人口风险函数的最小化器.
研究的目的:
- 提出一个关于无限维度风险最小化器的非参数推理的一般框架.
- 扩展得分测试方法,以处理复杂的函数估计问题.
- 证明拟议框架在各种统计挑战中具有广泛的适用性.
主要方法:
- 利用估计数的表示作为人口风险函数的最小化器.
- 开发一个非参数扩展的得分测试,以推断风险最小化.
- 将框架应用于非参数和部分加法模型下的平均回归函数.
主要成果:
- 建议的框架被证明适用于广泛的问题.
- 分析和计算示例说明了该方法对平均回归的实用性.
- 模拟用于评估开发的程序的操作特征.
结论:
- 一般框架提供了一个强大的工具,用于对风险最小化器的非参数推理.
- 非参数得分测试扩展为复杂模型中的校准推理提供了可行的解决方案.
- 潜在的应用包括评估效果异质性,密度推断和条件独立性测试.
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