Regression analysis of compartmental models

Herein we study the problem of assessing, on the basis of noisy and incomplete observations, how much information there is in the data for model identification in compartmental systems. The underlying concept is that of an "information distance" between competing models, and estimation of this distance on the basis of the given data is discussed. Useful reduction of the dimensionality of the corresponding least squares problem is accomplished by regarding the decay rate constants as primary parameters of interest and the other parameters of the model as nuisance parameters. Estimation of the decay rate function is also discussed.