文章基本信息

标题：A Mixture Model for Rare and Clustered Populations Under Adaptive Cluster Sampling
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作者：Kelly C. M. Gonçalves ; Fernando A. S. Moura
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2016
卷号：11
期号：2
页码：519-544
DOI：10.1214/15-BA961
语种：English
出版社：International Society for Bayesian Analysis
摘要：Rare populations, such as endangered species, drug users and individuals infected by rare diseases, tend to cluster in regions. Adaptive cluster designs are generally applied to obtain information from clustered and sparse populations. The aim of this work is to propose a unit-level mixture model for clustered and sparse populations when the data are obtained from an adaptive cluster sample. Our approach considers heterogeneity among units belonging to different clusters. The proposed model is evaluated using simulated data and a real experiment in which adaptive samples were drawn from an enumeration of a waterfowl species in a 5,000 km2 area of central Florida. The results show that the model is efficient under many settings, even when the level of heterogeneity is low.