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\iteman{io-port 05957530}
\itemau{Donohue, M.C.; Overholser, R.; Xu, R.; Vaida, F.}
\itemti{Conditional Akaike information under generalized linear and proportional hazards mixed models.}
\itemso{Biometrika 98, No. 3, 685-700 (2011).}
\itemab
Summary: We study model selection for clustered data, when the focus is on cluster specific inference. Such data are often modelled using random effects, and a conditional Akaike information was proposed by {\it F. Vaida} and {\it S. Blanchard} [ibid. 92, No. 2, 351--370 (2005; Zbl 1094.62077)] and used to derive an information criterion under linear mixed models. We extend the approach to generalized linear and proportional hazards mixed models. Outside the normal linear mixed models, exact calculations are not available and we resort to asymptotic approximations. In the presence of nuisance parameters, a profile conditional Akaike information is proposed. Bootstrap methods are considered for their potential advantage in finite samples. Simulations show that the performance of the bootstrap and the analytic criteria are comparable, with bootstrap demonstrating some advantages for larger cluster sizes. The proposed criteria are applied to two cancer data sets to select models when the cluster-specific inference is of interest.
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\itemut{Akaike information; conditional likelihood; effective degrees of freedom}
\itemli{doi:10.1093/biomet/asr023}
\end