A note on conditional Akaike information for Poisson regression with random effects
A popular model selection approach for generalized linear mixed- effects models is the Akaike information criterion, or AIC. Among others, [7] pointed out the distinction between the marginal and conditional infer- ence depending on the focus of research. The conditional AIC was derived for the...
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| Format: | Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/98306 http://hdl.handle.net/10220/13261 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | A popular model selection approach for generalized linear mixed-
effects models is the Akaike information criterion, or AIC. Among others,
[7] pointed out the distinction between the marginal and conditional infer-
ence depending on the focus of research. The conditional AIC was derived
for the linear mixed-effects model which was later generalized by [5]. We
show that the similar strategy extends to Poisson regression with random
effects, where conditional AIC can be obtained based on our observations.
Simulation studies demonstrate the usage of the criterion. |
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