Journal article
An Akaike information criterion for model selection in the presence of incomplete data
Journal of statistical planning and inference, Vol.67(1), pp.45-65
1998
DOI: 10.1016/S0378-3758(97)00115-8
Abstract
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 Shimodaira (1994, in: Selecting Models from Data: Artificial Intelligence and Statistics IV, Lecture Notes in Statistics, vol. 89, Springer, New York, pp. 21–29). 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 Meng and Rubin (Journal of the American Statistical Association 86 (1991) 899–909). 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.
Details
- Title: Subtitle
- An Akaike information criterion for model selection in the presence of incomplete data
- Creators
- Joseph E Cavanaugh - Department of Statistics, University of Missouri, Columbia, MO 65211, USARobert H Shumway - Division of Statistics, University of California, Davis, CA 95616, USA
- Resource Type
- Journal article
- Publication Details
- Journal of statistical planning and inference, Vol.67(1), pp.45-65
- DOI
- 10.1016/S0378-3758(97)00115-8
- ISSN
- 0378-3758
- eISSN
- 1873-1171
- Publisher
- Elsevier B.V
- Language
- English
- Date published
- 1998
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984214846302771
Metrics
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