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Pré-Publication, Document De Travail Année : 2023

Spatial interpolation using mixture distributions: A Best Linear Unbiased Predictor

Interpolation spatiale de distributions de mélanges : Un meilleur prédicteur linéaire non biaisé

Résumé

This paper deals with three related problems in a geostatistical context. First, some data are available for given areas of the space rather than for some point locations which creates problems of multiscale areal data. Second, some uncertainties rely both on the input locations and on measured quantities at these locations, involving uncertainty propagation problems. Third, multidimensional outputs can be observed, with sometimes missing data. These three problems are addressed simultaneously here by considering mixtures of multivariate random fields and by adapting standard Kriging methodology to this context. While the usual Gaussian setting is lost, we show that conditional mean, variance and covariance can be derived from this specific setting. Case studies are presented both with simulated data and real data. In particular, we discuss the question of information loss in learning buildings energy efficiency.
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Dates et versions

hal-03276127 , version 1 (01-07-2021)
hal-03276127 , version 2 (25-08-2022)
hal-03276127 , version 3 (07-03-2023)
hal-03276127 , version 4 (17-01-2024)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

  • HAL Id : hal-03276127 , version 3

Citer

Marc Grossouvre, Didier Rullière, Jonathan Villot. Spatial interpolation using mixture distributions: A Best Linear Unbiased Predictor. 2023. ⟨hal-03276127v3⟩
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