Parameter estimation for nonlinear models with emphasis on compartmental models.

Techniques are described for the estimation of parameters in nonlinear models. When implemented in computer programs, these techniques will reduce programming effort, facilitate inference about implicit functions of parameters, and allow a more general variance-covariance structure. The techniques seem particularly useful for the analysis of data from stochastic compartmental models. They are illustrated by an example.