RE: Questions about identifiability

Dear Silke Usually the parameter structural identifiability problem is dealt with in terms of local identifiability (see Grewal MS, IEEE Trans.Autom.Control, 21:833-837, 1976) and less often in terms of global identifiability (by Laplace transformation, Taylor series expansion or similarity transformation/reparametrization). Different issues pertain to structural admissibility, reachability and observability. Keith Godfrey has researched extensively on these topics (start maybe with Godfrey KR and DiStefano JJ, 'Identifiability of model parameters' in 'Identifiability of parametric models', Walter E, Ed. 1987, Pergamon Press). IMHO, dealing with biological data, the identifiability problem becomes essentially local and is always pragmatic in nature. Hat matrix entries, information matrices and sensitivity to different initial estimates are usually valuable, but no warranty for structural identifiability (particularly global). Simulation exercises are always prone to what I use to call the 'mirror syndrome' and you may just get what you put in, ending up biased. Realization theory based on Markov parameters provides usually good insights for time-invariant finite-dimensional linear systems. My preferred approach is Information theory based on the concept that each observation has an intrinsic amount of information (Shannon Information) which is the upper bound for every inference exercise. But again, with routinely encountered problems we usually get away with small enough confidence intervals for our adjustable parameters. Cheers Luis --------------------------------------------------------------- Luis M. Pereira, Ph.D. Assistant Professor, Biopharmaceutics and Pharmacokinetics Massachusetts College of Pharmacy and Health Sciences 179 Longwood Ave, Boston, MA 02115 Phone: (617) 732-2905 Fax: (617) 732-2228 [EMAIL PROTECTED]