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|Title:||A note on conditional Akaike information for Poisson regression with random effects||Authors:||Lian, Heng||Issue Date:||2012||Source:||Lian, H. (2012). A note on conditional Akaike information for Poisson regression with random effects. Electronic Journal of Statistics, 6(0), 1-9.||Series/Report no.:||Electronic journal of statistics||Abstract:||A popular model selection approach for generalized linear mixed- effects models is the Akaike information criterion, or AIC. Among others,  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 . 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.||URI:||https://hdl.handle.net/10356/98306
|ISSN:||1935-7524||DOI:||http://dx.doi.org/10.1214/12-EJS665||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SPMS Journal Articles|
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