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Investigating the impact of operating parameters on molecular weight distributions using functional regression
Author(s) -
Hutchinson R.A.,
McLellan P.J.,
Ramsay J.O.,
Sulieman H.,
Bacon D.W.
Publication year - 2004
Publication title -
macromolecular symposia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 76
eISSN - 1521-3900
pISSN - 1022-1360
DOI - 10.1002/masy.200450238
Subject(s) - regression , regression analysis , discretization , functional data analysis , mathematics , characterization (materials science) , biological system , function (biology) , computer science , statistics , materials science , mathematical analysis , nanotechnology , biology , evolutionary biology
Molecular weight distributions (MWDs) are inherently functional observations in which differential weight fraction is expressed as a function of chain length. Conventional approaches for analyzing and predicting MWDs include discretization and treatment as multi‐response estimation problems, characterization using moments, and detailed mechanistic modeling to predict fractions for each chain length. However, these approaches can be sensitive to loss of information, complexity and problem conditioning. An alternative is to treat the MWDs as functional observations, and to use techniques from Functional Data Analysis (FDA), notably functional regression. The objective of this paper is to develop and apply empirical modeling techniques based on functional regression for investigating the impact of operating parameters on MWDs.