Extended information criterion (EIC) approach for linear mixed effects models under restricted maximum likelihood (REML) estimation
| Autor: | Takashi Funatogawa, Makio Ishiguro, Akifumi Yafune |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Statistics and Probability
Estimation Likelihood Functions Purpura Thrombocytopenic Idiopathic Linear mixed effect model Biometry Platelet Count Epidemiology Restricted maximum likelihood Model selection Growth Covariance Generalized linear mixed model Statistics Linear Models Mixed effects Humans Variance components Longitudinal Studies Mathematics |
| Zdroj: | Statistics in Medicine. 24:3417-3429 |
| ISSN: | 1097-0258 0277-6715 |
| DOI: | 10.1002/sim.2191 |
| Popis: | In clinical data analysis, the restricted maximum likelihood (REML) method has been commonly used for estimating variance components in the linear mixed effects model. Under the REML estimation, however, it is not straightforward to compare several linear mixed effects models with different mean and covariance structures. In particular, few approaches have been proposed for the comparison of linear mixed effects models with different mean structures under the REML estimation. We propose an approach using extended information criterion (EIC), which is a bootstrap-based extension of AIC, for comparing linear mixed effects models with different mean and covariance structures under the REML estimation. We present simulation studies and applications to two actual clinical data sets. |
| Databáze: | OpenAIRE |
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