A STATISTICAL VIEW OF SOME CHEMOMETRICS REGRESSION TOOLS
A STATISTICAL VIEW OF SOME CHEMOMETRICS REGRESSION TOOLS
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DOI：
10.2307/1269656
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发表时间：
1993-05-01
期刊：
影响因子：
2.5
通讯作者：
FRIEDMAN, JH
中科院分区：
文献类型：
--
作者：
FRANK, IE;FRIEDMAN, JH
化学计量学是化学的一个领域，研究统计方法在化学数据分析中的应用。除了从统计学和工程文献中借用许多技术外，化学计量学本身也产生了一些新的数据分析方法。本文从统计学的角度探讨了化学计量学中两种常用的预测建模方法-偏最小二乘法和主成分回归。目的是试图了解这些方法的明显成功之处，以及在哪些情况下可以预期它们会很好地发挥作用，并将它们与针对这些情况的其他统计方法进行比较。这些方法包括普通最小二乘法、变量子集选择和岭回归。
Chemometrics is a field of chemistry that studies the application of statistical methods to chemical data analysis. In addition to borrowing many techniques from the statistics and engineering literatures, chemometrics itself has given rise to several new data-analytical methods. This article examines two methods commonly used in chemometrics for predictive modeling-partial least squares and principal components regression-from a statistical perspective. The goal is to try to understand their apparent successes and in what situations they can be expected to work well and to compare them with other statistical methods intended for those situations. These methods include ordinary least squares, variable subset selection, and ridge regression.