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Wang, D. ; Stapor, P. ; Hasenauer, J.

Dirac mixture distributions for the approximation of mixed effects models.

IFAC PapersOnline 52, 200-206 (2019)
Verlagsversion DOI
Open Access Gold
Mixed effect modeling is widely used to study cell-to-cell and patient-to-patient variability. The population statistics of mixed effect models is usually approximated using Dirac mixture distributions obtained using Monte-Carlo, quasi Monte-Carlo, and sigma point methods. Here, we propose the use of a method based on the Cramér-von Mises Distance, which has been introduced in the context of filtering. We assess the accuracy of the different methods using several problems and provide the first scalability study for the Cramér-von Mises Distance method. Our results indicate that for a given number of points, the method based on the modified Cramér-von Mises Distance method tends to achieve a better approximation accuracy than Monte-Carlo and quasi Monte-Carlo methods. In contrast to sigma-point methods, the method based on the modified Cramér-von Mises Distance allows for a flexible number of points and a more accurate approximation for nonlinear problems.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Differential Equations ; Dirac Mixture Distribution ; Mixed Effect Model ; Monte Carlo Method ; Sigma Point Method
Sprache
Veröffentlichungsjahr 2019
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 2405-8963
e-ISSN 1474-6670
Zeitschrift IFAC-PapersOnLine
Quellenangaben Band: 52, Heft: 26, Seiten: 200-206 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Frankfurt ; München [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-553800-001
DOI 10.1016/j.ifacol.2019.12.258
Scopus ID 85081097449
Erfassungsdatum 2020-05-12
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