Spatial Spread Sampling Using Weakly Associated Vectors

Raphaël Jauslin () and Yves Tillé
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Raphaël Jauslin: University of Neuchatel

Journal of Agricultural, Biological and Environmental Statistics, 2020, vol. 25, issue 3, No 8, 451 pages

Abstract: Abstract 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.

Keywords: GRTS; Local pivotal method; Cube method; Stratification (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s13253-020-00407-1

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