在对称正确定义矩阵的多重体上对收缩估计的实证贝叶斯方法
Chun-Hao Yang1, Hani Doss2, Baba C Vemuri3
1Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan.
Journal of the American Statistical Association
|April 9, 2024
概括
我们为多重值数据开发了新的收缩估计器,特别是对对称正确定义矩阵上的Log-Normal分布. 这些估计器在统计推理任务中表现优于最大概率估计器 (MLE).
科学领域:
- 统计 统计 统计 统计
- 多重学习多重学习
- 矩阵分析是一门学科.
背景情况:
- 詹姆斯-斯坦估计器增强了多变量正常平均值的估计,而不是最大概率估计器 (MLE).
- 收缩估计得到了充分的研究,但对多重值数据来说是有限的.
- 对称的正确定义矩阵在各种应用中形成了至关重要的多元组.
研究的目的:
- 在N x N对称正确定义矩阵的多重上提出对Log-normal分布的新型收缩估计器.
- 为了它的计算效率和已建立的应用,使用Log-Euclidean度量.
- 开发一个分析衍生的收缩估计器,它是异常最优的.
主要方法:
- 定义对称正确定义矩阵的多重体上的Log-normal分布.
- 在损失函数中使用Log-Euclidean度量和距离.
- 在分析形式中推导一个收缩估计器.
主要成果:
- 拟议的收缩估计器在平方误差损失下主导样本Fréchet平均值 (MLE).
- 估计器被证明是异常最优的.
- 与现有方法相比,模拟显示出更高的性能.
结论:
- 新型收缩估计器为多重值的Log-Normal数据提供了改进的统计推断.
- 该方法对扩散和功能磁共振成像 (dMRI/fMRI) 的应用具有前景.
- 这项工作将收缩估计扩展到一个复杂的,高维的数据领域.
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