How to construct a multiple regression model for data with missing elements and outlying objects

• Published in: Analytica chimica acta Vol. 581; no. 2; pp. 324 - 332
• Main Authors: Stanimirova, Ivana, Serneels, Sven, Van Espen, Pierre J., Walczak, Beata
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 09.01.2007; Elsevier
• Subjects: Analytical chemistry; Chemistry; Chemometrics; Exact sciences and technology; General, instrumentation; Missing data; Modelling; Outliers; Partial least squares (PLS); Partial robust M-regression (PRM); Robust regression; Outliers; Missing data; Partial robust M-regression (PRM); Partial least squares (PLS); Modelling; Robust regression; Chemometrics; Performance evaluation; Algorithm; Multiple regression; Outlier; PLS regression; Regression model; Comparative study
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2006.08.014

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.