Abstract
Recent developments in nonparametric marginal likelihood have generated a very general, but readily operationalized, method of overcoming the nuisance parameter problem in stochastic models. Theoretical, empirical, and simulation analyses show that the nonparametric approach seriously undermines the modeling advantages traditionally associated with the mover-stayer model. Moreover, the goodness-of-fit success often achieved by the mover-stayer model is shown to have a plausible explanation not requiring a true mover/stayer dichotomy in the population.
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