Corpus ID: 73728788

Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data

@article{Chung2019FunctionalPC,
  title={Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data},
  author={Seokhyun Chung and R. Kontar},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.03871}
}
  • Seokhyun Chung, R. Kontar
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • The advance of modern sensor technologies enables collection of multi-stream longitudinal data where multiple signals from different units are collected in real-time. In this article, we present a non-parametric approach to predict the evolution of multi-stream longitudinal data for an in-service unit through borrowing strength from other historical units. Our approach first decomposes each stream into a linear combination of eigenfunctions and their corresponding functional principal component… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 41 REFERENCES
    Nonparametric Modeling and Prognosis of Condition Monitoring Signals Using Multivariate Gaussian Convolution Processes
    8
    Scalable prognostic models for large-scale condition monitoring applications
    13
    An adaptive functional regression-based prognostic model for applications with missing data
    26
    Functional Data Analysis for Sparse Longitudinal Data
    883
    Functional Data Analysis
    2370
    Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data.
    256
    Principal components analysis of sampled functions
    215
    A Geometric Approach to Maximum Likelihood Estimation of the Functional Principal Components From Sparse Longitudinal Data
    77