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On the behaviour of marginal and conditional AIC in linear mixed models
Open accessAbstract
In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion, aic, have been used, based either on the marginal or on the conditional distribution. We show that the marginal aic is not an asymptotically unbiased estimator of the Akaike information, and favours smaller models without random effects. For the conditional aic, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that can lead to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional aic, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package (R Development Core Team, 2010) is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.
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Smoothing Parameter and Model Selection for General Smooth Models
Wood, S. N., Pya, N., & Säfken, B. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1180986 · Cited by 1,312
Smoothing Parameter and Model Selection for General Smooth Models
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Model Selection in Linear Mixed Models
Müller, S., Scealy, J. L., & Welsh, A. H. (2013) · Statistical Science · doi:10.1214/12-sts410 · Cited by 324
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Ciuti, S., Muhly, T. B., Paton, D. G., McDevitt, A. D., Musiani, M., & Boyce, M. S. (2012) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2012.1483 · Cited by 284
Metadata
- Authors
- Greven, S., & Kneib, T.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-07-27
- Volume
- 97
- Issue
- 4
- Pages
- 773-789
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2030553221
- Cited by
- 253
- References
- 24
- FWCI
- 9.43
- Type
- journal article
- Language
- en
- Open access
- green
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