A note on conditional Akaike information for Poisson regression with random effects
| dc.creator | Lian, Heng | |
| dc.date | 2008-10-11 | |
| dc.date.accessioned | 2026-07-07T10:09:27Z | |
| dc.date.available | 2026-07-07T10:09:27Z | |
| dc.description | A popular model selection approach for generalized linear mixed-effects models is the Akaike information criterion, or AIC. Among others, \cite{vaida05} pointed out the distinction between the marginal and conditional inference depending on the focus of research. The conditional AIC was derived for the linear mixed-effects model which was later generalized by \cite{liang08}. We show that the similar strategy extends to Poisson regression with random effects, where condition AIC can be obtained based on our observations. Simulation studies demonstrate the usage of the criterion. | |
| dc.description | 7 pages, 1 figure | |
| dc.identifier | https://arxiv.org/abs/0810.2010 | |
| dc.identifier | http://arxiv.org/abs/0810.2010 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/171322 | |
| dc.subject | Methodology | |
| dc.title | A note on conditional Akaike information for Poisson regression with random effects | |
| dc.type | text |