Bayesian Analysis

Model-based inferences from adaptive cluster sampling

V. E. Rapley and A. H. Welsh

Full-text: Open access

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)

Article information

Source
Bayesian Anal., Volume 3, Number 4 (2008), 717-736.

Dates
First available in Project Euclid: 22 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1340370406

Digital Object Identifier
doi:10.1214/08-BA327

Mathematical Reviews number (MathSciNet)
MR2469797

Zentralblatt MATH identifier
1330.62068

Keywords
Informative sampling MCMC spatial sampling zero-inflated count data

Citation

Rapley, V. E.; Welsh, A. H. Model-based inferences from adaptive cluster sampling. Bayesian Anal. 3 (2008), no. 4, 717--736. doi:10.1214/08-BA327. https://projecteuclid.org/euclid.ba/1340370406


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