Model-based Inference for Rare and Clustered Populations from Adaptive Cluster Sampling using Auxiliary Variables
نویسندگان
چکیده
Abstract Rare populations, such as endangered animals and plants, drug users individuals with rare diseases, tend to cluster in regions. Adaptive sampling is generally applied obtain information from clustered sparse populations since it increases survey effort areas where the of interest are observed. This work proposes a unit-level model which assumes that counts related auxiliary variables, improving process by assigning different weights cells, besides referring them spatially. The proposed fits grouped arranged on regular grid Bayesian framework. approach compared alternative methods using simulated data real experiment adaptive samples were drawn an African buffalo population 24,108 square kilometer area East Africa. Simulation studies show efficient several settings, validating method this paper for practical situations.
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Adaptive survey designs for sampling rare and clustered populations
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ژورنال
عنوان ژورنال: Journal of Survey Statistics and Methodology
سال: 2021
ISSN: ['2325-0984', '2325-0992']
DOI: https://doi.org/10.1093/jssam/smab001