Parameter estimation: local identifiability of parameters

Abstract

For biological systems one often cannot set up experiments to measure all of the state variables. If only a subset of the state variables can be measured, it is possible that some of the system parameters cannot influence the measured state variables or that they do so in combinations that do not define the parameters' effects separately. Such parameters are unidentifiable and are in theory unestimable. Given a model of the system, linear or nonlinear, and initial estimates of the values of all parameters, we exhibit a simple theory and describe a program for checking the local identifiability of the parameters at the initial estimates for given experiments on the model. The program, IDENT, is available from the authors.