Model-based Inference for Rare and Clustered Populations from Adaptive Cluster Sampling using Auxiliary Variables
Conference
65th ISI World Statistics Congress 2025
Format: CPS Abstract - WSC 2025
Keywords: deflation or inflation of zeros, informative_sampling, mcmc
Session: CPS 19 - Genomics and Conservation
Monday 6 October 5:10 p.m. - 6:10 p.m. (Europe/Amsterdam)
Abstract
Rare populations, such as endangered animals and plants, drug users, and individuals with rare diseases, tend to cluster in regions. Adaptive cluster sampling is generally applied to obtain information from clustered and sparse populations since it increases survey effort in areas where the individuals of interest are observed. This work proposes a unit-level model that assumes counts are related to auxiliary variables, improving the sampling process, assigning different weights to the cells, and referring them spatially. The proposed model fits rare and grouped populations, disposed over a regular grid, in a Bayesian framework. The approach is compared to alternative methods using simulated data and a real experiment in which adaptive samples were drawn from an African Buffaloes population in 24,108 square kilometers of East Africa. Simulation studies show that the model is efficient under several settings, validating the methodology proposed in this paper for practical situations.