SCREAM: A novel method for multi-way regression problems with shifts and shape changes in one mode
Some fields where calibration of multi-way data is required, such as hyphenated chromatography, can suffer of high inaccuracy when traditional N-PLS is used, due to the presence of shifts or peak shape changes in one of the modes. To overcome this problem, a new regression method for multi-way data called SCREAM (Shifted Covariates REgression Analysis forMulti-way data), which is based on a combination of PARAFAC2 and principal covariates regression (PCovR), is proposed. In particular, the algorithm combines a PARAFAC2 decomposition of the X array and a PCovR-like way of computing the regression coefficients, analogously to what has been described by Smilde and Kiers (A.K. Smilde and H.A.L. Kiers, 1999) in the case of other multi-way PCovR models. Themethod is tested on real and simulated datasets providing good results and performing as well or better than other available regression approaches for multi-way data.
Reference:
If you use SCREAM, we would appreciate a link to
F. Marini and R. Bro. SCREAM: A novel method for multi-way regression problems with shifts and shape changes in one mode. Chemometr. Intell. Lab. Syst. 129:64-75, 2013.
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