Expectation–maximization algorithm for regression, deconvolution and smoothing of shot‐noise limited data
Expectation–maximization algorithm for regression, deconvolution and smoothing of shot‐noise limited data
复制标题
用于散粒噪声有限数据的回归、反卷积和平滑的期望最大化算法
DOI：
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发表时间：
1991
期刊：
影响因子：
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通讯作者：
S. Bialkowski
中科院分区：
文献类型：
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作者：
S. Bialkowski
本文给出了一种简单的小噪声限制数据的反卷积和回归算法。该算法易于适应几乎任何模型，并收敛于全局最优。用多分量谱回归、谱反卷积和平滑的例子来说明该算法。给出了一种基于Fisher信息矩阵确定参数不确定性的算法和方法，并通过三个实例进行了说明。给出了二极管阵列太阳光谱辐射计的摄谱仪光栅阶数补偿的实验实例，说明了该技术在环境分析中的应用。该算法具有稳定性好、简单、数据量守恒、收敛性好等优点。
A simple algorithm for deconvolution and regression of shot‐noise‐limited data is illustrated in this paper. The algorithm is easily adapted to almost any model and converges to the global optimum. Multiple‐component spectrum regression, spectrum deconvolution and smoothing examples are used to illustrate the algorithm. The algorithm and a method for determining uncertainties in the parameters based on the Fisher information matrix are given and illustrated with three examples. An experimental example of spectrograph grating order compensation of a diode array solar spectroradiometer is given to illustrate the use of this technique in environmental analysis. The major advantages of the EM algorithm are found to be its stability, simplicity, conservation of data magnitude and guaranteed convergence.