Model Selection for Linear Mixed Models Using Predictive Criteria
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Publication:3625350
DOI10.1080/03610910802645362zbMath1290.62055OpenAlexW1976322966MaRDI QIDQ3625350
Publication date: 12 May 2009
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610910802645362
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)
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Uses Software
Cites Work
- Fixed-effect variable selection in linear mixed models using \(R^2\) statistics
- Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
- Estimating the dimension of a model
- A Concordance Correlation Coefficient to Evaluate Reproducibility
- Two-Stage Analysis Based on a Mixed Model: Large-Sample Asymptotic Theory and Small-Sample Simulation Results
- Regression and time series model selection in small samples
- A comparison of two approaches for selecting covariance structures in the analysis of repeated measurements
- Goodness-of-Fit in Generalized Nonlinear Mixed-Effects Models
- Approximate F-tests of multiple degree of freedom hypotheses in generalized least squares analyses of unbalanced split-plot experiments
- Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood
- The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
- Performance of the Kenward–Roger Method when the Covariance Structure is Selected Using AIC and BIC
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