Statistical inference for random T-tessellations models: application to agricultural landscape modeling. - INSA Rennes - Institut National des Sciences Appliquées de Rennes
Communication Dans Un Congrès Année : 2024

Statistical inference for random T-tessellations models: application to agricultural landscape modeling.

Résumé

The Gibbsian T-tessellation models allow the representation of a wide range of spatial patterns. In this talk we present statistical tools for these models and illustrate their application to the comparison of three agricultural landscapes in France. Model parameters are estimated via Monte Carlo Maximum Likelihood based on an adapted Metropolis-Hastings-Green dynamics. In order to reduce the computational costs, a pseudolikelihood estimate is used for the initialization of the likelihood optimization. Model assessment is based on global envelope tests applied to the set of functional statistics of tessellation.
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hal-04669756 , version 1 (09-08-2024)

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  • HAL Id : hal-04669756 , version 1

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Katarzyna Adamczyk-Chauvat, Mouna Kassa, Julien Papaïx, Kiên Kiêu, Radu S. Stoica. Statistical inference for random T-tessellations models: application to agricultural landscape modeling.. 11th International Conference on Spatio-Temporal Modelling METMA XI, Lancaster University, Jul 2024, Lancaster, United Kingdom. ⟨hal-04669756⟩
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