Abstract

A new method for sampling from a finite population that is spread in one, two or more dimensions is presented. Weights are used to create strong negative correlations between the inclusion indicators of nearby units. The method can be used to produce unequal probability samples that are well spread over the population in every dimension, without any spatial stratification. Since the method is very general there are numerous possible applications, especially in sampling of natural resources where spatially balanced sampling has proven to be efficient. Two examples show that the method gives better estimates than other commonly used designs. (C) 2011 Elsevier B.V. All rights reserved.

Keywords

Correlated Poisson sampling; Generalized Random-Tessellation Stratified design; Negative correlation; Spatial sampling; Spatially balanced sampling; Unequal probability sampling

Published in

Journal of Statistical Planning and Inference
2012, volume: 142, number: 1, pages: 139-147
Publisher: ELSEVIER SCIENCE BV

Authors' information