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How to construct a multiple regression model for data with missing elements and outlying objects
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DOI：10.1016/j.aca.2006.08.014.png)
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摘要
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The aim of this study is to show the usefulness of robust multiple regression techniques implemented in the expectation maximization framework in order to model successfully data containing missing elements and outlying objects. In particular, results from a comparative study of partial least squares and partial robust M-regression models implemented in the expectation maximization algorithm are presented. The performances of the proposed approaches are illustrated on simulated data with and without outliers, containing different percentages of missing elements and on a real data set. The obtained results suggest that the proposed methodology can be used for constructing satisfactory regression models in terms of their trimmed root mean squared errors. (c) 2006 Elsevier B.V. All rights reserved.
Keyword:
modelling
chemometrics
robust regression
outliers
missing data
partial least squares (PLS)
partial robust M-regression (PRM)
期刊
IF:
6
论文数:
3.3W
被引数:
6.1W
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暂无机构信息
引用论文
Spatial sign preprocessing: A simple way to impart moderate robustness to multivariate estimators
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