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Linear models for statistical shape analysis based on parametrized closed curves

Author

Listed:
  • Luis Gutiérrez

    (Pontificia Universidad Católica de Chile)

  • Ramsés H. Mena

    (Universidad Nacional Autónoma de México)

  • Carlos Díaz-Avalos

    (Universidad Nacional Autónoma de México)

Abstract

We develop a simple, yet powerful, technique based on linear regression models of parametrized closed curves which induces a probability distribution on the planar shape space. Such parametrization is driven by control points which can be estimated from the data. Our proposal is capable to infer about the mean shape, to predict the shape of an object at an unobserved location, and, while doing so, to consider the effect of predictors on the shape. In particular, the model is able to detect possible differences across the levels of the predictor, thus also applicable for two-sample tests. A simple MCMC algorithm for Bayesian inference is also presented and tested with simulated and real datasets. Supplementary material is available online.

Suggested Citation

  • Luis Gutiérrez & Ramsés H. Mena & Carlos Díaz-Avalos, 2020. "Linear models for statistical shape analysis based on parametrized closed curves," Statistical Papers, Springer, vol. 61(3), pages 1213-1229, June.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:3:d:10.1007_s00362-018-0986-0
    DOI: 10.1007/s00362-018-0986-0
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    References listed on IDEAS

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