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Unfalsified model parametrization based on frequency domain noise information

✍ Scribed by Tong Zhou


Book ID
108307588
Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
189 KB
Volume
36
Category
Article
ISSN
0005-1098

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The structure of noise in a dataset and, in particular, whether it is homoscedastic or heteroscedastic, can significantly affect the properties of multivariate calibration models. This is particularly true when the data are subjected to a nonlinear transformation prior to the formation of the model.