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Spatial Spread Sampling Using Weakly Associated Vectors

Raphaël Jauslin & Yves Tillé

Résumé Geographical data are generally autocorrelated. In this case, it is preferable to select spread units. In this paper, we propose a new method for selecting well-spread samples from a finite spatial population with equal or unequal inclusion probabilities. The proposed method is based on the definition of a spatial structure by using a stratification matrix. Our method exactly satisfies given inclusion probabilities and provides samples that are very well spread. A set of simulations shows that our method outperforms other existing methods such as the generalized random tessellation stratified or the local pivotal method. Analysis of the variance on a real dataset shows that our method is more accurate than these two. Furthermore, a variance estimator is proposed.
   
Mots-clés GRTS; Local pivotal method; Cube method; Stratification
   
Citation Jauslin, R., & Tillé, Y. (2020). Spatial Spread Sampling Using Weakly Associated Vectors. Journal of Agricultural, Biological, and Environmental Statistics, 25(3), 431-451.
   
Type Article de périodique (Anglais)
Date de publication 11-8-2020
Nom du périodique Journal of Agricultural, Biological, and Environmental Statistics
Volume 25
Numéro 3
Pages 431-451
URL https://link.springer.com/article/10.1007/s13253-020-00407-1