MODEL SELECTION IN RING-RECOVERY MODELS USING SCORE TESTS

A strategy is outlined for selecting models for ring-recovery data usi ng score tests. The approach is particularly valuable in avoiding unne cessary fitting of complicated, multiparameter models to data that do not require models of such complexity. Difficulties of convergence of iterative methods and potential boundary-estimation problems are there by reduced. Data analyzed in Freeman and Morgan (1992, Biometrics 48, 217-236) are reanalyzed using score tests. These tests are repeated us ing both numerical and symbolic differentiation and also using both ob served and expected information. We recommend using the expected infor mation, and find that numerical differentiation is as good as symbolic differentiation. Motivated by the need to-describe a wide range of mo dels succinctly, we also provide a new general notation for ring-recov ery models.