Longitudinal data model selection

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• 作者：Rahman Azari ; Lexin Li and Chih-Ling Tsai
• 关键词：Model selection ; Akaike information criterion ; Bayesian information criterion ; Kullback– ; Leibler discrepancy ; Longitudinal data
• 刊名：Computational Statistics and Data Analysis
• 出版年：2006
• 出版时间：20 July 2006
• 年：2006
• 卷：50
• 期：11
• 页码：3053-3066
• 全文大小：377 K

文摘

In longitudinal data with correlated errors, we apply the likelihood and residual likelihood approaches to obtain the corrected Akaike information criterion (AICc) and the residual information criterion (RIC), respectively. Simulation studies show that AICc outperforms the Akaike information criterion (AIC) when the numbers of subjects and repeated observations are small, and RIC is superior to the Bayesian information criterion (BIC) when the signal-to-noise ratio is moderate to large. We illustrate the practical use of these selection criteria with an empirical example for modeling the serum cholesterol measured at six time occasions.