The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Model Selection in Linear Mixed Models
Open accessAbstract
Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model selection, which often aims to choose a parsimonious model with other desirable properties from a possibly very large set of candidate statistical models. Over the last 5–10 years the literature on model selection in linear mixed models has grown extremely rapidly. The problem is much more complicated than in linear regression because selection on the covariance structure is not straightforward due to computational issues and boundary problems arising from positive semidefinite constraints on covariance matrices. To obtain a better understanding of the available methods, their properties and the relationships between them, we review a large body of literature on linear mixed model selection. We arrange, implement, discuss and compare model selection methods based on four major approaches: information criteria such as AIC or BIC, shrinkage methods based on penalized loss functions such as LASSO, the Fence procedure and Bayesian techniques.
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Cites
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Rao, J. N. K. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0731 · Cited by 2,049
One-stage dose–response meta-analysis for aggregated data
Crippa, A., Discacciati, A., Bottai, M., Spiegelman, D., & Orsini, N. (2019) · Statistical Methods in Medical Research · doi:10.1177/0962280218773122 · Cited by 365
Linear and generalized linear mixed models
Bolker, B. M. (2015) · Ecological Statistics · doi:10.1093/acprof:oso/9780199672547.003.0014 · Cited by 339
van Eeuwijk, F. A., Bustos-Korts, D., Millet, E. J., Boer, M. P., Kruijer, W., Thompson, A., Malosetti, M., Iwata, H., Quiroz, R., Kuppe, C., et al. (2019) · Plant Science · doi:10.1016/j.plantsci.2018.06.018 · Cited by 217
Space-time logic of liver gene expression at sub-lobular scale
Droin, C., Kholtei, J. E., Bahar Halpern, K., Hurni, C., Rozenberg, M., Muvkadi, S., Itzkovitz, S., & Naef, F. (2021) · Nature Metabolism · doi:10.1038/s42255-020-00323-1 · Cited by 148
Metadata
- Authors
- Müller, S., Scealy, J. L., & Welsh, A. H.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2013-05-01
- Volume
- 28
- Issue
- 2
- Pages
- 135-167
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W1985497040
- Cited by
- 324
- References
- 78
- FWCI
- 24.37
- Type
- journal article
- Language
- en
- Open access
- green
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