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Spatial Spread Sampling Using Weakly Associated Vectors
Date de parution
2020-8-11
In
Journal of Agricultural, Biological, and Environmental Statistics
Vol.
3
No
25
De la page
431
A la page
451
Revu par les pairs
1
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.
Identifiants
Autre version
https://link.springer.com/article/10.1007/s13253-020-00407-1
Type de publication
journal article
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