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Functional Multiple-Outcome Model in Application to Multivariate Growth Curves of Infant Data

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Abstract

Motivated by a medical study that attempts to analyze the relationship between growth curves and other variables and to measure the association among multiple growth curves, the authors develop a functional multiple-outcome model to decompose the total variation of multiple functional outcomes into variation explained by independent variables with time-varying coefficient functions, by latent factors and by noise. The latent factors are the hidden common factors that influence the multiple outcomes and are found through the combined functional principal component analysis approach. Through the coefficients of the latent factors one may further explore the association of the multiple outcomes. This method is applied to the multivariate growth data of infants in a real medical study in Shanghai and produces interpretable results. Convergence rates for the proposed estimates of the varying coefficient and covariance functions of the model are derived under mild conditions.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou, 221116, China

    Xingyu Yan & Peng Zhao

  2. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China

    Yingchun Zhou & Xiaolong Pu

Authors
  1. Xingyu Yan
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  2. Yingchun Zhou
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  3. Xiaolong Pu
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  4. Peng Zhao
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Corresponding author

Correspondence to Yingchun Zhou.

Additional information

This paper was supported by the National Natural Science Foundation of China under Grant Nos. 11771146, 11831008, 81530086, 11771145, 11871252, the 111 Project (B14019) and Program of Shanghai Subject Chief Scientist under Grant No. 14XD1401600.

This paper was recommended for publication by Editor LI Qizhai.

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Yan, X., Zhou, Y., Pu, X. et al. Functional Multiple-Outcome Model in Application to Multivariate Growth Curves of Infant Data. J Syst Sci Complex 34, 1555–1577 (2021). https://doi.org/10.1007/s11424-020-9319-1

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  • DOI: https://doi.org/10.1007/s11424-020-9319-1

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