Abstract
In Chemometrics it is often the norm to develop regression methods for analysing non-linear multivariate data by using the observations (measurements) as the sole constraint. This is the case regardless of the nature of the regression method (parametric or non-parametric)[1]. In this article we present the development of a regression model using data assimilation[2] – A technique that takes into account additional available information about the “system” which the model is to represent. The new approach shows substantial improvement over the “conventional” methods[3] against which it has been compared.
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Mussa, H.Y., Lary, D.J., Glen, R.C. (2006). Building Structure-Property Predictive Models Using Data Assimilation. In: R. Berthold, M., Glen, R.C., Fischer, I. (eds) Computational Life Sciences II. CompLife 2006. Lecture Notes in Computer Science(), vol 4216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875741_7
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DOI: https://doi.org/10.1007/11875741_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45767-1
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