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Adaptive Web Sampling
Author(s) -
Thompson Steven K.
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00576.x
Subject(s) - resampling , computer science , inference , sampling (signal processing) , markov chain , sampling design , sample (material) , statistic , population , markov chain monte carlo , adaptive sampling , data mining , statistics , machine learning , artificial intelligence , mathematics , monte carlo method , filter (signal processing) , computer vision , bayesian probability , chemistry , demography , chromatography , sociology
Summary A flexible class of adaptive sampling designs is introduced for sampling in network and spatial settings. In the designs, selections are made sequentially with a mixture distribution based on an active set that changes as the sampling progresses, using network or spatial relationships as well as sample values. The new designs have certain advantages compared with previously existing adaptive and link‐tracing designs, including control over sample sizes and of the proportion of effort allocated to adaptive selections. Efficient inference involves averaging over sample paths consistent with the minimal sufficient statistic. A Markov chain resampling method makes the inference computationally feasible. The designs are evaluated in network and spatial settings using two empirical populations: a hidden human population at high risk for HIV/AIDS and an unevenly distributed bird population.