Robust regression techniques - A useful alternative for the detection of outlier data in chemical analysis
Robust regression techniques - A useful alternative for the detection of outlier data in chemical analysis
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DOI：
10.1016/j.talanta.2005.12.058
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发表时间：
2006-10-15
期刊：
影响因子：
6.1
通讯作者：
Herrero, Ana
中科院分区：
文献类型：
--
作者：
Ortiz, M. Cruz;Sarabia, Luis A.;Herrero, Ana
分析方法的验证是指对一些性能标准的评价，如准确度、灵敏度、线性范围、检测能力、选择性、校准曲线等。这意味着使用不同的统计方法，其中一些与统计回归技术有关，这些方法可能具有耐用性或不耐用性。本文综述了分析化学中常用的稳健校正方法：Huber M-估计，Andrews，Tukey和Welsh GM-估计，模糊估计，约束M-估计，CM，GM-估计，GM最小裁剪平方法本文还表明，最小中值平方（LMS）回归的数学性质可以在检测异常数据在化学分析中的极大兴趣。将这些回归方法应用于合成数据和真实的数据，并对所得结果进行了比较分析。也有一些应用程序的审查，这种强大的回归工程在一个合适的和简单的方式，证明非常有用的，以确保客观检测离群值。ISO 5725-5建议使用稳健回归。(c)2006 Elsevier B. V.保留所有权利。
The validation of an analytical procedure means the evaluation of some performance criteria such as accuracy, sensitivity, linear range, capability of detection, selectivity, calibration curve, etc. This implies the use of different statistical methodologies, some of them related with statistical regression techniques, which may be robust or not. The presence of outlier data has a significant effect on the determination of sensitivity, linear range or capability of detection amongst others, when these figures of merit are evaluated with non-robust methodologies.In this paper some of the robust methods used for calibration in analytical chemistry are reviewed: the Huber M-estimator; the Andrews, Tukey and Welsh GM-estimators; the fuzzy estimators; the constrained M-estimators, CM; the least trimmed squares, LTS. The paper also shows that the mathematical properties of the least median squares (LMS) regression can be of great interest in the detection of outlier data in chemical analysis. A comparative analysis is made of the results obtained by applying these regression methods to synthetic and real data. There is also a review of some applications where this robust regression works in a suitable and simple way that proves very useful to secure an objective detection of outliers. The use of a robust regression is recommended in ISO 5725-5. (c) 2006 Elsevier B.V. All rights reserved.