Spatially Balanced Sampling of Continuous Populations

Abstract

When sampling from a continuous population (or distribution), we often want a rather small sample due to some cost attached to processing the sample or to collecting information in the field. Moreover, a probability sample that allows for design-based statistical inference is often desired. Given these requirements, we want to reduce the sampling variance of the Horvitz-Thompson estimator as much as possible. To achieve this, we introduce different approaches to using the local pivotal method for selecting well-spread samples from multidimensional continuous populations. The results of a simulation study clearly indicate that we succeed in selecting spatially balanced samples and improve the efficiency of the Horvitz-Thompson estimator.

Keywords

local pivotal method; spatial balance; spatial sampling

Published in

Scandinavian Journal of Statistics
2018, volume: 45, number: 3, pages: 792-805
Publisher: WILEY

SLU Authors

• Grafström, Anton

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.1111/sjos.12322

Permanent link to this page (URI)

https://res.slu.se/id/publ/96607