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Selecting mixed-effects models based on a generalized information criterion

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  • Pu, Wenji
  • Niu, Xu-Feng

Abstract

The generalized information criterion (GIC) proposed by Rao and Wu [A strongly consistent procedure for model selection in a regression problem, Biometrika 76 (1989) 369-374] is a generalization of Akaike's information criterion (AIC) and the Bayesian information criterion (BIC). In this paper, we extend the GIC to select linear mixed-effects models that are widely applied in analyzing longitudinal data. The procedure for selecting fixed effects and random effects based on the extended GIC is provided. The asymptotic behavior of the extended GIC method for selecting fixed effects is studied. We prove that, under mild conditions, the selection procedure is asymptotically loss efficient regardless of the existence of a true model and consistent if a true model exists. A simulation study is carried out to empirically evaluate the performance of the extended GIC procedure. The results from the simulation show that if the signal-to-noise ratio is moderate or high, the percentages of choosing the correct fixed effects by the GIC procedure are close to one for finite samples, while the procedure performs relatively poorly when it is used to select random effects.

Suggested Citation

  • Pu, Wenji & Niu, Xu-Feng, 2006. "Selecting mixed-effects models based on a generalized information criterion," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 733-758, March.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:3:p:733-758
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    References listed on IDEAS

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    1. Pötscher, B.M., 1991. "Effects of Model Selection on Inference," Econometric Theory, Cambridge University Press, vol. 7(2), pages 163-185, June.
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    Cited by:

    1. Zangdong He & Wanzhu Tu & Sijian Wang & Haoda Fu & Zhangsheng Yu, 2015. "Simultaneous variable selection for joint models of longitudinal and survival outcomes," Biometrics, The International Biometric Society, vol. 71(1), pages 178-187, March.
    2. Simona Buscemi & Antonella Plaia, 2020. "Model selection in linear mixed-effect models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 529-575, December.
    3. Peng, Heng & Lu, Ying, 2012. "Model selection in linear mixed effect models," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 109-129.
    4. Julia Braun & Leonhard Held & Bruno Ledergerber, 2012. "Predictive Cross-validation for the Choice of Linear Mixed-Effects Models with Application to Data from the Swiss HIV Cohort Study," Biometrics, The International Biometric Society, vol. 68(1), pages 53-61, March.
    5. Abhik Ghosh & Magne Thoresen, 2018. "Non-concave penalization in linear mixed-effect models and regularized selection of fixed effects," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(2), pages 179-210, April.
    6. Ping Wu & Xinchao Luo & Peirong Xu & Lixing Zhu, 2017. "New variable selection for linear mixed-effects models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(3), pages 627-646, June.
    7. Cantoni, Eva & Jacot, Nadège & Ghisletta, Paolo, 2024. "Review and comparison of measures of explained variation and model selection in linear mixed-effects models," Econometrics and Statistics, Elsevier, vol. 29(C), pages 150-168.
    8. Francisco M. M. Rocha & Julio M. Singer, 2018. "Selection of terms in random coefficient regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(2), pages 225-242, January.
    9. Dimova, Rositsa B. & Markatou, Marianthi & Talal, Andrew H., 2011. "Information methods for model selection in linear mixed effects models with application to HCV data," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2677-2697, September.
    10. Howard D. Bondell & Arun Krishna & Sujit K. Ghosh, 2010. "Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models," Biometrics, The International Biometric Society, vol. 66(4), pages 1069-1077, December.
    11. Chih-Hao Chang & Hsin-Cheng Huang & Ching-Kang Ing, 2022. "Inference of random effects for linear mixed-effects models with a fixed number of clusters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(6), pages 1143-1161, December.

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