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Estimation of spatial-functional based-line logit model for multivariate longitudinal data

Author

Listed:
  • Tengteng Xu

    (East China Normal University
    MOE, East China Normal University)

  • Riquan Zhang

    (East China Normal University
    MOE, East China Normal University)

  • Xiuzhen Zhang

    (East China Normal University
    MOE, East China Normal University
    Shanxi Datong University)

Abstract

In this paper, a novel method is proposed to analyze multivariate longitudinal data that contains spatial location information. The method has the advantage of analyzing the relationship between curves at neighbor time points and observing the relationship between locations. We offer the spatial covariance function and use functional PCA to estimate unknown parameter functions. A detail solving process and theoretical properties are introduced. Based on the gradient descent method and leave-one-out cross-validation method, we estimate those unknown parameters and select the principal components respectively. Furthermore, compared with other four methods, the proposed method shows a better category effect on simulation studies and air quality data analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:1:d:10.1007_s00180-022-01217-4
    DOI: 10.1007/s00180-022-01217-4
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    References listed on IDEAS

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