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The performance of least squares and robust regression in the calibration of analytical methods under non‐normal noise distributions
Author(s) -
Wolters R.,
Kateman G.
Publication year - 1989
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180030203
Subject(s) - robust regression , calibration , mathematics , total least squares , statistics , kurtosis , ordinary least squares , skewness , monte carlo method , regression , least squares function approximation , regression analysis , least trimmed squares , linear regression , estimator
By means of Monte Carlo simulations a comparison has been made between ordinary least squares regression and robust regression. The robust regression procedure is based on the Huber estimate and is computed by means of the iteratively reweighted least squares algorithm. The performance of both procedures has been evaluated for estimation of the parameters of a calibration function and for determination of the concentration of unknown samples. The influence of the distributional characteristics skewness and kurtosis has been studied, and the number of measurements used for constructing the calibration curve has also been taken into account. Under certain conditions robust regression offers an advantage over least squares regression.