Model Selection in Ring-Recovery Models Using Score Tests

Abstract

A strategy is outlined for selecting models for ring-recovery data using score tests. The approach is particularly valuable in avoiding unnecessary 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 thereby reduced. Data analyzed in Freeman and Morgan (1992, Biometrics 48, 217-236) are reanalyzed using score tests. These tests are repeated using both numerical and symbolic differentiation and also using both observed and expected information. We recommend using the expected information, and find that numerical differentiation is as good as symbolic differentiation. Motivated by the need to-describe a wide range of models succinctly, we also provide a new general notation for ring-recovery models.