Translator Disclaimer
December 2008 Model-based inferences from adaptive cluster sampling
V. E. Rapley, A. H. Welsh
Bayesian Anal. 3(4): 717-736 (December 2008). DOI: 10.1214/08-BA327

Abstract

Adaptive cluster sampling is useful for exploring populations of rare plant and animal species which cluster together because it allows sampling effort to be concentrated in areas where observed values are high. This allows more useful data to be collected with less effort than simpler sampling methods which ignore the population structure. In this paper, we take a model based approach in a Bayesian framework to make inference about the number of individuals in a sparse, clustered population. This approach allows us to use knowledge of the population to inform both the sampling design and inference, thereby making coherent use of the data in the analysis and resulting in improved population estimates. The methodology is compared to the design-based modified Horvitz-Thompson estimator through analysis of the examples presented in the defining paper of Thompson (1990)

Citation

Download Citation

V. E. Rapley. A. H. Welsh. "Model-based inferences from adaptive cluster sampling." Bayesian Anal. 3 (4) 717 - 736, December 2008. https://doi.org/10.1214/08-BA327

Information

Published: December 2008
First available in Project Euclid: 22 June 2012

zbMATH: 1330.62068
MathSciNet: MR2469797
Digital Object Identifier: 10.1214/08-BA327

Rights: Copyright © 2008 International Society for Bayesian Analysis

JOURNAL ARTICLE
20 PAGES


SHARE
Vol.3 • No. 4 • December 2008
Back to Top