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

Authors
Publisher
Elsevier B.V.
Publication Date
Volume
187
Identifiers
DOI: 10.1016/s0003-2670(00)82910-4

Abstract

Abstract 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.

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