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Model selection using modified AIC and BIC in joint modeling of paired functional data

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  • Wei, Jiawei
  • Zhou, Lan

Abstract

A modified version of the Akaike information criterion and two modified versions of the Bayesian information criterion are proposed to select the number of principal components and to choose the penalty parameters of penalized splines in a joint model of paired functional data. Numerical results show that, compared with an existing procedure using the cross-validation, the procedure based on the information criteria is computationally much faster while giving a similar performance.

Suggested Citation

  • Wei, Jiawei & Zhou, Lan, 2010. "Model selection using modified AIC and BIC in joint modeling of paired functional data," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1918-1924, December.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:23-24:p:1918-1924
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    References listed on IDEAS

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    1. Bradley Efron, 2004. "The Estimation of Prediction Error: Covariance Penalties and Cross-Validation," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 619-632, January.
    2. repec:cup:cbooks:9780521780506 is not listed on IDEAS
    3. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    4. repec:cup:cbooks:9780521785167 is not listed on IDEAS
    5. John A. Rice & Colin O. Wu, 2001. "Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves," Biometrics, The International Biometric Society, vol. 57(1), pages 253-259, March.
    6. Lan Zhou & Jianhua Z. Huang & Raymond J. Carroll, 2008. "Joint modelling of paired sparse functional data using principal components," Biometrika, Biometrika Trust, vol. 95(3), pages 601-619.
    Full references (including those not matched with items on IDEAS)

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