Journal
BIOMETRICS
Volume 65, Issue 3, Pages 928-936Publisher
WILEY
DOI: 10.1111/j.1541-0420.2008.01172.x
Keywords
Diagnostics; Goodness of fit; Neural dynamics; Nonlinear dynamics
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P>This article investigates the problem of model diagnostics for systems described by nonlinear ordinary differential equations (ODEs). I propose modeling lack of fit as a time-varying correction to the right-hand side of a proposed differential equation. This correction can be described as being a set of additive forcing functions, estimated from data. Representing lack of fit in this manner allows us to graphically investigate model inadequacies and to suggest model improvements. I derive lack-of-fit tests based on estimated forcing functions. Model building in partially observed systems of ODEs is particularly difficult and I consider the problem of identification of forcing functions in these systems. The methods are illustrated with examples from computational neuroscience.
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