Unser M, Ellis J R, Pun T, Eden M
J Microsc. 1987 Mar;145(Pt 3):245-56.
In quantitative electron energy loss spectrometry, it is desirable to estimate the background law below core edge energy in a way that provides the maximum signal-to-noise ratio. Assuming an inverse power background model and independently Poisson distributed measurements, it is shown how to achieve this goal by using a maximum likelihood (ML) estimation technique which provides unbiased and minimum mean square error estimates of all parameters of interest. An efficient and computationally stable implementation of this procedure is proposed. Standard logarithmic least squares estimations are then compared with the ML approach and the gain in performance due to optimal processing is quantified.
在定量电子能量损失谱分析中，希望以能提供最大信噪比的方式来估计核心边缘能量以下的背景规律。假设采用逆幂背景模型且测量值服从独立泊松分布，本文展示了如何通过使用最大似然（ML）估计技术来实现这一目标，该技术能对所有感兴趣的参数提供无偏且最小均方误差估计。本文还提出了此过程的一种高效且计算稳定的实现方法。然后将标准对数最小二乘估计与ML方法进行比较，并对因最优处理而带来的性能提升进行量化。
