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一个基于信托的信任区间,用于多项式概率的线性组合

Katherine A Batterton¹, Christine M Schubert¹, Richard L Warr²

¹Department of Mathematics and Statistics, Air Force Institute of Technology, Ohio, USA.

Biometrical journal. Biometrische Zeitschrift

|September 11, 2023

概括

这项研究引入了一种新的信托方法,用于对多项概率的小样本统计推理. 该方法提供可靠的置信区间,在模拟中表现优于传统技术,用于改进诊断分类器选择.

科学领域:

统计统计统计统计
多项式概率分析
在信托推论中,信托推论是指信托推论.

背景情况:

系统结果通常被总结为混或偶然表中的概率,当结果超过2时,代表多项实验.
目前用于多项概率线性组合的统计推理方法主要集中在大样本和参数设置上.
在小样本环境中,迫切需要可靠的推断方法,这在试点或低成本研究中很常见.

研究的目的:

为了弥补小样本统计推理对多项概率的差距.
使用信托方法来推导多项参数的线性组合的置信区间.
为在多参数设置中应用信托方法建立一个逻辑框架.

主要方法:

利用信托方法开发对多项参数线性组合的信任区间.
开发了一个新的逻辑框架,用于将信托方法扩展到多参数设置.
进行模拟,以评估拟议方法的性能与引导和大样本方法相比.

主要成果:

拟议的信托方法证明了可取的频客属性,保持最低覆盖水平.
模拟显示,信托方法实现了与其他方法相比的间隔长度,但覆盖范围更高.
该方法在医学应用中成功说明了其实用性,用于在诊断慢性异构脏病时选择分类器.

结论:

新型信托方法为小样本多项设置中的统计推理提供了强大的方法.
这项工作将信托方法扩展到多参数问题,提供了显著的方法进步.
开发的方法为各种应用,特别是医疗保健中的绩效评估和分类器选择提供了有价值的替代方案.

关键词:

贝叶斯成本是贝叶斯成本.慢性全图性脏病慢性全图性脏病信托性推断的推断这是一个多维的多维空间.一个小样本的小样本.

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