A generalized mover.stayer model for panel data

A generalized mover.stayer model is described for conditionally Markov processes under panel observation. Marginally the model represents a mixture of nested continuous.time Markov processes in which sub.models are defined by constraining some transition intensities to zero between two or more states of a full model.A Fisher scoring algorithm is described which facilitates maximum likelihood estimation based only on the first derivatives of the transition probability matrices.The model is fit to data from a smoking prevention study and is shown to provide a significant improvement in fit over a time.homogeneous Markov model.Extensions are developed which facilitate examination of covariate effects on both the transition intensities and the mover.stayer probabilities.