Generalized degrees of freedom and adaptive model selection in linear mixed-effects models

DOI10.1016/J.CSDA.2011.09.001zbMath1239.62083OpenAlexW2057149823WikidataQ35545564 ScholiaQ35545564MaRDI QIDQ425656

Sunni L. Mumford, Bo Zhang, Xiaotong Shen

Publication date: 8 June 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3214646

zbMATH Keywords

adaptive penaltyloss estimation

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

On generalized degrees of freedom with application in linear mixed models selection ⋮ Computing AIC for black-box models using generalized degrees of freedom: A comparison with cross-validation

Uses Software

• S-PLUS
• alr3
• MEMSS

Cites Work

• Random-Effects Models for Longitudinal Data
• One-step sparse estimates in nonconcave penalized likelihood models
• Estimating the dimension of a model
• The risk inflation criterion for multiple regression
• Regression and time series model selection in small samples
• On Measuring and Correcting the Effects of Data Mining and Model Selection
• The Covariance Inflation Criterion for Adaptive Model Selection
• An example of using mixed models and proc mixed for longitudinal data
• Adaptive Model Selection
• Mixed-Effects Models in S and S-PLUS
• Applied Linear Regression
• The Estimation of Prediction Error
• Optimal Model Assessment, Selection, and Combination
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