Information based model selection criterion for binary response generalized linear mixed models
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012 |
| Pages | 57-61 |
| Publication status | Published - 2012 |
Conference
| Title | 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012 |
|---|---|
| Place | China |
| City | Harbin, Heilongjiang |
| Period | 23 - 26 June 2012 |
Link(s)
Abstract
Conditional Akaike information criterion is derived within the framework of conditional-likelihood-based method for binary response generalized linear mixed models. The criterion essentially is the asymptotically unbiased estimator of conditional Akaike information based on maximum likelihood estimator. The proposed criterion is adopted to address the model selection problems in binary response generalized linear mixed models. Comparing with other Monte-Carlo EM based methods, conditional Akaike information criterion is more flexible and computationally attractive. Simulations show that the performance of the proposed criterion is in general promising. The use of the criterion is demonstrated in the analysis of the chronic asthmatic patients data. © 2012 IEEE.
Research Area(s)
- Binary response, Conditional Akaike information, Generalized linear mixed model, Model selection
Citation Format(s)
Information based model selection criterion for binary response generalized linear mixed models. / Yu, Dalei; Yau, Kelvin K.W.; Ding, Chang.
Proceedings of the 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012. 2012. p. 57-61 6274678.Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
