Model selection for partial least squares regression
Model selection for partial least squares regression
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DOI：
10.1016/s0169-7439(02)00051-5
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发表时间：
2002-10-28
影响因子：
3.9
通讯作者：
Martin, EB
中科院分区：
文献类型：
--
作者：
Li, BB;Morris, J;Martin, EB
偏最小二乘（PLS）回归是一种强大的、经常应用于过程变量高度相关的多变量统计过程控制的技术。选择潜在变量的数量来建立一个有代表性的模型是一个重要的问题。化学计量学家经常使用Wold的R标准来确定潜在变量的数量，而最近一些统计学家提倡使用赤池信息标准（AIC）。本文在仿真研究的基础上，比较了Wold's R准则和AIC在选择包含在PLS模型中的潜在变量数量方面的比较，该模型将构成多元统计过程控制表示的基础。结果表明，Wold’s R准则和AIC准则都没有表现出令人满意的性能。这与调整后的Wold’s R标准形成对比，后者在选择已知真实模型的次数方面表现出令人满意的性能。然后用两个工业应用来演示该方法。第一个涉及使用来自工业流化床反应器的数据对产品质量进行建模，第二个侧重于工业近红外数据集，结果与模拟研究的结果一致。(C) 2002 Elsevier Science B.V.版权所有
Partial least squares (PLS) regression is a powerful and frequently applied technique in multivariate statistical process control when the process variables are highly correlated. Selection of the number of latent variables to build a representative model is an important issue. A metric frequently used by chemometricians for the determination of the number of latent variables is that of Wold's R criterion, whilst more recently a number of statisticians have advocated the use of Akaike Information Criterion (AIC). In this paper, a comparison between Wold's R criterion and AIC for the selection of the number of latent variables to include in a PLS model that will form the basis of a multivariate statistical process control representation is undertaken based on a simulation study. It is shown that neither Wold's R criterion nor AIC exhibit satisfactory performance. This is in contrast to the adjusted Wold's R criteria which is shown to demonstrate satisfactory performance in terms of the number of times the known true model is selected. Two industrial applications are then used to demonstrate the methodology. The first relates to the modelling of a product quality using data from an industrial fluidised bed reactor and the second focuses on an industrial NIR data set, The results are consistent with those of the simulation studies. (C) 2002 Elsevier Science B.V. All rights reserved.