A nonparametric vs. latent class model of general practitioner utilization: Evidence from Canada

The goodness-of-fit results show the kernel conditional density estimator provides a better fit to the observed distribution of GP visits than the latent class negative binomial model. There are some meaningful differences in how the predicted conditional mean number of GP visits changes with a change in an individual's characteristics, called the incremental effect (IE), between the kernel conditional density estimator and the latent class negative binomial model. The most notable differences are observed in the right tail of the distribution where the IEs from the latent class negative binomial model are up to 190 times the magnitude of the IEs from the kernel conditional density estimator.