A note on conditional AIC for linear mixed-effects models

• 成果类型: Article
• 署名作者: Liang, Hua; Wu, Hulin; Zou, Guohua
• 署名单位: University of Rochester; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asn023
• 发表日期: 2008
• 页码: 773778
• 关键词: akaike information; selection; regression

摘要

The conventional model selection criterion, the Akaike information criterion, AIC, has been applied to choose candidate models in mixed-effects models by the consideration of marginal likelihood. Vaida & Blanchard (2005) demonstrated that such a marginal AIC and its small sample correction are inappropriate when the research focus is on clusters. Correspondingly, these authors suggested the use of conditional AIC. Their conditional AIC is derived under the assumption that the variance-covariance matrix or scaled variance-covariance matrix of random effects is known. This note provides a general conditional AIC but without these strong assumptions. Simulation studies show that the proposed method is promising.