کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10524940 | 957866 | 2005 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Model selection criteria based on Kullback information measures for nonlinear regression
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 134, Issue 2, 1 October 2005, Pages 332-349
Journal: Journal of Statistical Planning and Inference - Volume 134, Issue 2, 1 October 2005, Pages 332-349
نویسندگان
Hyun-Joo Kim, Joseph E. Cavanaugh,