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An Akaike information criterion for model selection in the presence of incomplete data. (English) Zbl 1067.62504
Summary: We derive and investigate a variant of AIC, the Akaike information criterion, for model selection in settings where the observed data is incomplete. Our variant is based on the motivation provided for the PDIO (”predictive divergence for incomplete observation models”) criterion of H. Shimodaira [in: Selecting Models from Data: Artificial Intelligence and Statistics IV, Lect. Notes Stat. 89, 21–29 (1994; Zbl 0828.62004)]. However, our variant differs from PDIO in its ”goodness-of-fit” term. Unlike AIC and PDIO, which require the computation of the observed-data empirical log-likelihood, our criterion can be evaluated using only complete-data tools, readily available through the EM algorithm and the SEM (”supplemented” EM) algorithm of X. Meng and D. Rubin [J. Am. Stat. Assoc. 86, 899–909 (1991)]. We compare the performance of our AIC variant to that of both AIC and PDIO in simulations where the data being modeled contains missing values. The results indicate that our criterion is less prone to overfitting than AIC and less prone to underfitting than PDIO.

MSC:
62B10 Statistical aspects of information-theoretic topics
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