Model selection criteria based on Kullback information measures for nonlinear regression

AICc has been justified for the nonlinear regression framework by Hurvich and Tsai (Biometrika 76 (1989) 297). In this paper, we justify KICc for this framework, and propose versions of AICI and KICI suitable for nonlinear regression applications. We evaluate the selection performance of AIC, AICc, AICI, KIC, KICc, and KICI in a simulation study. Our results generally indicate that the “improved” criteria outperform the “corrected” criteria, which in turn outperform the non-adjusted criteria. Moreover, the KIC family performs favorably against the AIC family.