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Subset selection for parameter estimation in an HIV model
Author(s) -
Fink Martin,
Attarian Adam,
Tran Hien
Publication year - 2007
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.200700319
Subject(s) - selection (genetic algorithm) , human immunodeficiency virus (hiv) , parameter space , estimation theory , model selection , nonlinear system , estimation , computer science , least squares function approximation , mathematical optimization , machine learning , mathematics , artificial intelligence , algorithm , biology , virology , statistics , engineering , physics , systems engineering , quantum mechanics , estimator
This paper discusses methodologies for subset selection for nonlinear least squares parameter estimation. In particular, we will present approaches for partitioning the parameter space into well‐conditioned and ill‐conditioned subsets. The algorithms are applied to a simplified mathematical model of the physiologic response of the human immunodeficiency virus (HIV) in humans. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)