Least median of squares: a robust method for outlier and model error detection in regression and calibration

The least median of squares method is a robust regression method, which means that it is not sensitive to outliers or other violations of the assumption of the usual normal model. This contrasts with the conventional regression method, which minimizes the sum of squares. It is demonstrated that the...

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Bibliographic Details
Published in:Analytica chimica acta 1986, Vol.187, p.171-179
Main Authors: Massart, Desire L., Kaufman, Leonard, Rousseeuw, Peter J., Leroy, Annick
Format: Article
Language:English
Subjects:
Analytical chemistry
Chemistry
Exact sciences and technology
Miscellaneous
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Summary:The least median of squares method is a robust regression method, which means that it is not sensitive to outliers or other violations of the assumption of the usual normal model. This contrasts with the conventional regression method, which minimizes the sum of squares. It is demonstrated that the proposed method can be used to detect or correct for outliers or model errors in calibration applications and in comparing two procedures.
ISSN:0003-2670
1873-4324
DOI:10.1016/S0003-2670(00)82910-4