Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10524940 | Journal of Statistical Planning and Inference | 2005 | 18 Pages |
Abstract
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.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Hyun-Joo Kim, Joseph E. Cavanaugh,