| Title | Smoothing parameter selection in quasi-likelihood models |
| Authors | Chiou, Jeng-Min Tsai, Chih-Ling |
| Affiliation | Acad Sinica, Inst Stat Sci, Taipei 11529, Taiwan. Univ Calif Davis, Grad Sch Management, Davis, CA 95616 USA. Peking Univ, Guanghua Sch Management, Beijing 100871, Peoples R China. |
| Keywords | akaike information criterion local quasi-likelihood generalized cross-validation smoothing parameter estimator GENERALIZED LINEAR-MODELS NONPARAMETRIC REGRESSION |
| Issue Date | 2006 |
| Publisher | journal of nonparametric statistics |
| Citation | JOURNAL OF NONPARAMETRIC STATISTICS.2006,18,(3),307-314. |
| Abstract | We derive an improved version of the Akaike information criterion (AICC) for quasi-likelihood models with nonparametric functions. This selection criterion is designed as approximately unbiased estimates of the expected Kullback-Leibler information for nonparametric functions under quasi-likelihood models. The finite sample performance of the AICC is demonstrated via Monte Carlo simulations for nonparametric logistic and Poisson regression models. The results show that AICC is better than both the Akaike information criterion and the generalized cross-validation critserion. |
| URI | http://hdl.handle.net/20.500.11897/252543 |
| ISSN | 1048-5252 |
| DOI | 10.1080/10485250600867182 |
| Indexed | SCI(E) |
| Appears in Collections: | 光华管理学院 |
