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Should We Condition on the Number of Points When Modelling Spatial Point Patterns?

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  • Jesper Møller
  • Ninna Vihrs

Abstract

We discuss the practice of directly or indirectly assuming a model for the number of points when modelling spatial point patterns even though it is rarely possible to validate such a model in practice because most point pattern data consist of only one pattern. We therefore explore the possibility to condition on the number of points instead when fitting and validating spatial point process models. In a simulation study with different popular spatial point process models, we consider model validation using global envelope tests based on functional summary statistics. We find that conditioning on the number of points will for some functional summary statistics lead to more narrow envelopes and thus stronger tests and that it can also be useful for correcting for some conservativeness in the tests when testing composite hypothesis. However, for other functional summary statistics, it makes little or no difference to condition on the number of points. When estimating parameters in popular spatial point process models, we conclude that for mathematical and computational reasons, it is convenient to assume a distribution for the number of points.

Suggested Citation

  • Jesper Møller & Ninna Vihrs, 2022. "Should We Condition on the Number of Points When Modelling Spatial Point Patterns?," International Statistical Review, International Statistical Institute, vol. 90(3), pages 551-562, December.
    • Handle: RePEc:bla:istatr:v:90:y:2022:i:3:p:551-562
    DOI: 10.1111/insr.12501
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    References listed on IDEAS

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    1. Mari Myllymäki & Tomáš Mrkvička & Pavel Grabarnik & Henri Seijo & Ute Hahn, 2017. "Global envelope tests for spatial processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 381-404, March.
    2. Frédéric Lavancier & Jesper Møller & Ege Rubak, 2015. "Determinantal point process models and statistical inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(4), pages 853-877, September.
    3. Adrian Baddeley & Jean-François Coeurjolly & Ege Rubak & Rasmus Waagepetersen, 2014. "Logistic regression for spatial Gibbs point processes," Biometrika, Biometrika Trust, vol. 101(2), pages 377-392.
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