A model-based initial guess for estimating parameters in systems of ordinary differential equations

Research output: Contribution to journalArticlepeer-review

Abstract

The inverse problem of parameter estimation from noisy observations is a major challenge in statistical inference for dynamical systems. Parameter estimation is usually carried out by optimizing some criterion function over the parameter space. Unless the optimization process starts with a good initial guess, the estimation may take an unreasonable amount of time, and may converge to local solutions, if at all. In this article, we introduce a novel technique for generating good initial guesses that can be used by any estimation method. We focus on the fairly general and often applied class of systems linear in the parameters. The new methodology bypasses numerical integration and can handle partially observed systems. We illustrate the performance of the method using simulations and apply it to real data.

Original languageEnglish
Pages (from-to)1176-1184
Number of pages9
JournalBiometrics
Volume71
Issue number4
DOIs
StatePublished - 1 Dec 2015

Bibliographical note

Publisher Copyright:
© 2015, The International Biometric Society.

Keywords

  • Differential equations
  • Nonlinear least squares
  • Optimization

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Statistics and Probability

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