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Spatial Regression With Partial Differential Equation Regularisation

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  • Laura M. Sangalli

Abstract

This work gives an overview of an innovative class of methods for the analysis of spatial and of functional data observed over complicated two‐dimensional domains. This class is based on regression with regularising terms involving partial differential equations. The associated estimation problems are solved resorting to advanced numerical analysis techniques. The synergical interplay of approaches from statistics, applied mathematics and engineering endows the methods with important advantages with respect to the available techniques, and makes them able to accurately deal with data structures for which the classical techniques are unfit. Spatial regression with differential regularisation is illustrated via applications to the analysis of eco‐colour doppler measurements of blood‐flow velocity, and to functional magnetic resonance imaging signals associated with neural connectivity in the cerebral cortex.

Suggested Citation

  • Laura M. Sangalli, 2021. "Spatial Regression With Partial Differential Equation Regularisation," International Statistical Review, International Statistical Institute, vol. 89(3), pages 505-531, December.
    • Handle: RePEc:bla:istatr:v:89:y:2021:i:3:p:505-531
    DOI: 10.1111/insr.12444
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    References listed on IDEAS

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    Cited by:

    1. Arnone, Eleonora & Ferraccioli, Federico & Pigolotti, Clara & Sangalli, Laura M., 2022. "A roughness penalty approach to estimate densities over two-dimensional manifolds," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    2. Ferraccioli, Federico & Sangalli, Laura M. & Finos, Livio, 2022. "Some first inferential tools for spatial regression with differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

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