Separable nonlinear least squares, applied to integral-matching ODE estimation, matches or beats traditional least squares in most simulated scenarios and runs substantially faster.
simode: R Package for statistical inference of ordinary differential equations using separable integral-matching
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
In this paper we describe simode: Separable Integral Matching for Ordinary Differential Equations. The statistical methodologies applied in the package focus on several minimization procedures of an integral-matching criterion function, taking advantage of the mathematical structure of the differential equations like separability of parameters from equations. Application of integral based methods to parameter estimation of ordinary differential equations was shown to yield more accurate and stable results comparing to derivative based ones. Linear features such as separability were shown to ease optimization and inference. We demonstrate the functionalities of the package using various systems of ordinary differential equations.
fields
stat.ME 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Separable nonlinear least-squares parameter estimation for complex dynamic systems
Separable nonlinear least squares, applied to integral-matching ODE estimation, matches or beats traditional least squares in most simulated scenarios and runs substantially faster.