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Joint modelling of paired sparse functional data using principal components

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  • Lan Zhou
  • Jianhua Z. Huang
  • Raymond J. Carroll

Abstract

We propose a modelling framework to study the relationship between two paired longitudinally observed variables. The data for each variable are viewed as smooth curves measured at discrete time-points plus random errors. While the curves for each variable are summarized using a few important principal components, the association of the two longitudinal variables is modelled through the association of the principal component scores. We use penalized splines to model the mean curves and the principal component curves, and cast the proposed model into a mixed-effects model framework for model fitting, prediction and inference. The proposed method can be applied in the difficult case in which the measurement times are irregular and sparse and may differ widely across individuals. Use of functional principal components enhances model interpretation and improves statistical and numerical stability of the parameter estimates. Copyright 2008, Oxford University Press.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:biomet:v:95:y:2008:i:3:p:601-619
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    File URL: http://hdl.handle.net/10.1093/biomet/asn035
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    Cited by:

    1. Li, Meng & Wang, Kehui & Maity, Arnab & Staicu, Ana-Maria, 2022. "Inference in functional linear quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    2. Ziyue Liu & Anne R. Cappola & Leslie J. Crofford & Wensheng Guo, 2014. "Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 108-118, March.
    3. Shin, Yei Eun & Zhou, Lan & Ding, Yu, 2022. "Joint estimation of monotone curves via functional principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 166(C).
    4. Lijie Gu & Li Wang & Wolfgang Härdle & Lijian Yang, 2014. "A simultaneous confidence corridor for varying coefficient regression with sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 806-843, December.
    5. Carmen D. Tekwe & Roger S. Zoh & Fuller W. Bazer & Guoyao Wu & Raymond J. Carroll, 2018. "Functional multiple indicators, multiple causes measurement error models," Biometrics, The International Biometric Society, vol. 74(1), pages 127-134, March.
    6. Shin, Hyejin & Lee, Seokho, 2015. "Canonical correlation analysis for irregularly and sparsely observed functional data," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 1-18.
    7. Lihui Zhao & Tom Chen & Vladimir Novitsky & Rui Wang, 2021. "Joint penalized spline modeling of multivariate longitudinal data, with application to HIV‐1 RNA load levels and CD4 cell counts," Biometrics, The International Biometric Society, vol. 77(3), pages 1061-1074, September.
    8. Haocheng Li & John Staudenmayer & Raymond J. Carroll, 2014. "Hierarchical functional data with mixed continuous and binary measurements," Biometrics, The International Biometric Society, vol. 70(4), pages 802-811, December.
    9. Li, Yehua & Qiu, Yumou & Xu, Yuhang, 2022. "From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    10. Jiguo Cao & Kunlaya Soiaporn & Raymond J. Carroll & David Ruppert, 2019. "Modeling and Prediction of Multiple Correlated Functional Outcomes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(1), pages 112-129, March.
    11. Kyunghee Han & Pantelis Z Hadjipantelis & Jane-Ling Wang & Michael S Kramer & Seungmi Yang & Richard M Martin & Hans-Georg Müller, 2018. "Functional principal component analysis for identifying multivariate patterns and archetypes of growth, and their association with long-term cognitive development," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
    12. Han Shang, 2014. "A survey of functional principal component analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(2), pages 121-142, April.
    13. Tengteng Xu & Riquan Zhang & Xiuzhen Zhang, 2023. "Estimation of spatial-functional based-line logit model for multivariate longitudinal data," Computational Statistics, Springer, vol. 38(1), pages 79-99, March.
    14. Cody Carroll & Hans‐Georg Müller & Alois Kneip, 2021. "Cross‐component registration for multivariate functional data, with application to growth curves," Biometrics, The International Biometric Society, vol. 77(3), pages 839-851, September.
    15. Mohammed Chowdhury & Colin Wu & Reza Modarres, 2018. "Nonparametric estimation of conditional distribution functions with longitudinal data and time-varying parametric models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(1), pages 61-83, January.
    16. Gertheiss, Jan & Goldsmith, Jeff & Staicu, Ana-Maria, 2017. "A note on modeling sparse exponential-family functional response curves," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 46-52.
    17. Hans-Georg Müller & Wenjing Yang, 2010. "Dynamic relations for sparsely sampled Gaussian processes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 1-29, May.
    18. Yuan Wang & Jianhua Hu & Kim-Anh Do & Brian P. Hobbs, 2019. "An Efficient Nonparametric Estimate for Spatially Correlated Functional Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 162-183, April.
    19. Guo, Mengmeng & Zhou, Lhan & Huang, Jianhua Z. & Härdle, Wolfgang Karl, 2013. "Functional data analysis of generalized quantile regressions," SFB 649 Discussion Papers 2013-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    20. Chen, Xuerong & Li, Haoqi & Liang, Hua & Lin, Huazhen, 2019. "Functional response regression analysis," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 218-233.
    21. repec:hum:wpaper:sfb649dp2014-002 is not listed on IDEAS
    22. 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.
    23. repec:hum:wpaper:sfb649dp2013-001 is not listed on IDEAS
    24. Meihua Wu & Ana Diez†Roux & Trivellore E. Raghunathan & Brisa N. Sánchez, 2018. "FPCA†based method to select optimal sampling schedules that capture between†subject variability in longitudinal studies," Biometrics, The International Biometric Society, vol. 74(1), pages 229-238, March.

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