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Curve Registration of Functional Data for Approximate Bayesian Computation

Author

Listed:
  • Anthony Ebert

    (Mathematics and Statistics for Medical Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
    ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Parkville, VIC 3052, Australia)

  • Kerrie Mengersen

    (Mathematics and Statistics for Medical Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
    ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Parkville, VIC 3052, Australia)

  • Fabrizio Ruggeri

    (ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Parkville, VIC 3052, Australia
    Consiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche, 20133 Milano, Italy)

  • Paul Wu

    (Mathematics and Statistics for Medical Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
    ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Parkville, VIC 3052, Australia)

Abstract

Approximate Bayesian computation is a likelihood-free inference method which relies on comparing model realisations to observed data with informative distance measures. We obtain functional data that are not only subject to noise along their y axis but also to a random warping along their x axis, which we refer to as the time axis. Conventional distances on functions, such as the L 2 distance, are not informative under these conditions. The Fisher–Rao metric, previously generalised from the space of probability distributions to the space of functions, is an ideal objective function for aligning one function to another by warping the time axis. We assess the usefulness of alignment with the Fisher–Rao metric for approximate Bayesian computation with four examples: two simulation examples, an example about passenger flow at an international airport, and an example of hydrological flow modelling. We find that the Fisher–Rao metric works well as the objective function to minimise for alignment; however, once the functions are aligned, it is not necessarily the most informative distance for inference. This means that likelihood-free inference may require two distances: one for alignment and one for parameter inference.

Suggested Citation

  • Anthony Ebert & Kerrie Mengersen & Fabrizio Ruggeri & Paul Wu, 2021. "Curve Registration of Functional Data for Approximate Bayesian Computation," Stats, MDPI, vol. 4(3), pages 1-14, September.
  • Handle: RePEc:gam:jstats:v:4:y:2021:i:3:p:45-775:d:630469
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    References listed on IDEAS

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