Previous Articles     Next Articles

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

  

  1. YAN Xingyu
    School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou 221116, China.
    ZHOU Yingchun (Corresponding author) · PU Xiaolong
    Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics,East China Normal University, Shanghai 200062, China. Email: yczhou@stat.ecnu.edu.cn.
    ZHAO Peng
    School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou 221116, China.
  • Online:2021-08-25 Published:2021-08-10

YAN Xingyu · ZHOU Yingchun · PU Xiaolong · ZHAO Peng. Functional Multiple-Outcome Model in Application to Multivariate Growth Curves of Infant Data[J]. Journal of Systems Science and Complexity, 2021, 34(4): 1555-1577.

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.
No related articles found!
Viewed
Full text


Abstract