Non-negative variance component estimation for the partial EIV model by the expectation maximization algorithm
Non-negative variance component estimation for the partial EIV model by the expectation maximization algorithm
复制标题
通过期望最大化算法对部分 EIV 模型进行非负方差分量估计
DOI：
10.1080/19475705.2020.1785955
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Qiwen Wu
中科院分区：
文献类型：
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作者：
Leyang Wang;Qiwen Wu
方差分量估计（VCE）的一个困难是估计可能变为负值，这在实际中是不可接受的。本文提出了两种新的非负VCE的方法，利用期望最大化算法的部分变量误差模型。前者用无约束估计准则搜索期望解，并在统计上得出结论，当其他VCE方法出现负估计时，方差分量确实移到了参数空间的边缘。我们专注于制定，并提供非负的分析，这个估计。特别是，后者的方法，它具有更高的计算效率，将是一个实际的替代现有的VCE型算法。此外，该方法易于实现，通过引入非负性约束自动支持非负方差分量。这两种算法都不需要复杂的矩阵求逆运算，降低了计算复杂度。实验结果表明，本文算法与其他VCE方法相比，具有较好的收敛性和一致性，后者能够快速估计参数，对于大数据量和多光谱数据处理具有实用价值。
Abstract A difficulty in variance component estimation (VCE) is that the estimates may become negative, which is not acceptable in practice. This article presents two new methods for non-negative VCE that utilize the expectation maximization algorithm for the partial errors-in-variables model. The former searches for the desired solutions with unconstrained estimation criterion and concludes statistically that the variance components have indeed moved to the edge of the parameter space when negative estimates appear implemented by the other existing VCE methods. We concentrate on the formulation and provide non-negative analysis of this estimator. In particularly, the latter approach, which has greater computational efficiency, would be a practical alternative to the existing VCE-type algorithms. Additionally, this approach is easy to implement, the non-negative variance components are automatically supported by introducing non-negativity constraints. Both algorithms are free from a complex matrix inversion and reduce computational complexity. The results show that our algorithms retrieve well to achieve identical estimates over the other VCE methods, the latter approach can quickly estimate parameters and has practical aspects for the large volume and multisource data processing.