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Bayesian size-and-shape regression modelling

Author

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  • Di Noia, Antonio
  • Mastrantonio, Gianluca
  • Jona Lasinio, Giovanna

Abstract

Building on Dryden et al. (2021), this note presents the Bayesian estimation of a regression model for size-and-shape response variables with Gaussian landmarks. Our proposal fits into the framework of Bayesian latent variable models and, potentially, allows for a highly flexible modelling framework.

Suggested Citation

  • Di Noia, Antonio & Mastrantonio, Gianluca & Jona Lasinio, Giovanna, 2024. "Bayesian size-and-shape regression modelling," Statistics & Probability Letters, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:stapro:v:204:y:2024:i:c:s0167715223001529
    DOI: 10.1016/j.spl.2023.109928
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    References listed on IDEAS

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    1. Ian L. Dryden & Kwang-Rae Kim & Huiling Le, 2019. "Bayesian Linear Size-and-Shape Regression with Applications to Face Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 83-103, February.
    2. Peter J. Green & Kanti V. Mardia, 2006. "Bayesian alignment using hierarchical models, with applications in protein bioinformatics," Biometrika, Biometrika Trust, vol. 93(2), pages 235-254, June.
    3. Ian L. Dryden & Alfred Kume & Phillip J. Paine & Andrew T. A. Wood, 2021. "Regression Modeling for Size-and-Shape Data Based on a Gaussian Model for Landmarks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 1011-1022, April.
    4. Kim Kenobi & Ian L. Dryden & Huiling Le, 2010. "Shape curves and geodesic modelling," Biometrika, Biometrika Trust, vol. 97(3), pages 567-584.
    Full references (including those not matched with items on IDEAS)

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