How to avoid over-fitting in multivariate calibration -: The conventional validation approach and an alternative
How to avoid over-fitting in multivariate calibration -: The conventional validation approach and an alternative
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DOI：
10.1016/j.aca.2007.05.030
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发表时间：
2007-07-09
影响因子：
6.2
通讯作者：
Rajko, R.
中科院分区：
文献类型：
--
作者：
Faber, N. M.;Rajko, R.
本文批判性地回顾了多变量校准中的过度拟合问题以及传统的基于验证的方法来避免该问题。它提出了一种随机化测试，使人们能够评估进入模型的每个组件的统计显着性。将该替代方案与交叉验证和独立测试集验证进行比较，以使用偏最小二乘 (PLS) 回归校准近红外光谱数据集。结果表明，替代方法更加客观，因为与基于验证的方法不同，它不需要使用“软”决策规则。因此，替代方法似乎是对化学计量学家工具箱的有用补充。 (c) 2007 Elsevier B.V. 保留所有权利。
This paper critically reviews the problem of over-fitting in multivariate calibration and the conventional validation-based approach to avoid it. It proposes a randomization test that enables one to assess the statistical significance of each component that enters the model. This alternative is compared with cross-validation and independent test set validation for the calibration of a near-infrared spectral data set using partial least squares (PLS) regression. The results indicate that the alternative approach is more objective, since, unlike the validation-based approach, it does not require the use of 'soft' decision rules. The alternative approach therefore appears to be a useful addition to the chemometrician's toolbox. (c) 2007 Elsevier B.V. All rights reserved.