论文标题
化学计量学中强大的多元方法
Robust multivariate methods in Chemometrics
论文作者
论文摘要
本章介绍了鲁棒统计的介绍，并具有化学计量性的应用。在描述了强大统计数据背后的基本思想和概念（包括如何构想稳健的估计器）之后，本章构建了稳健替代方案的构建（和使用），用于在化学计量学上经常使用的多变量分析方法，例如主成分分析和部分最小二乘。然后，本章提供了有关如何使用或扩展到分类的这些鲁棒方法的见解。总而言之，正在解决结果验证的问题：显示出与可靠估计相关的不确定性陈述。
This chapter presents an introduction to robust statistics with applications of a chemometric nature. Following a description of the basic ideas and concepts behind robust statistics, including how robust estimators can be conceived, the chapter builds up to the construction (and use) of robust alternatives for some methods for multivariate analysis frequently used in chemometrics, such as principal component analysis and partial least squares. The chapter then provides an insight into how these robust methods can be used or extended to classification. To conclude, the issue of validation of the results is being addressed: it is shown how uncertainty statements associated with robust estimates, can be obtained.
