A HIERARCHICAL DEPENDENT DIRICHLET PROCESS PRIOR FOR MODELLING BIRD MIGRATION PATTERNS IN THE UK

• 成果类型: Article
• 署名作者: Diana, Alex; Matechou, Eleni; Griffin, Jim; Johnston, Alison
• 署名单位: University of Kent; University of London; University College London; Cornell University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/19-AOAS1315
• 发表日期: 2020
• 页码: 473-493
• 关键词: population-size; CLIMATE-CHANGE; abundance

摘要

Environmental changes in recent years have been linked to phenological shifts which in turn are linked to the survival of species. The work in this paper is motivated by capture-recapture data on blackcaps collected by the British Trust for Ornithology as part of the Constant Effort Sites monitoring scheme. Blackcaps overwinter abroad and migrate to the UK annually for breeding purposes. We propose a novel Bayesian nonparametric approach for expressing the bivariate density of individual arrival and departure times at different sites across a number of years as a mixture model. The new model combines the ideas of the hierarchical and the dependent Dirichlet process, allowing the estimation of site-specific weights and year-specific mixture locations, which are modelled as functions of environmental covariates using a multivariate extension of the Gaussian process. The proposed modelling framework is extremely general and can be used in any context where multivariate density estimation is performed jointly across different groups and in the presence of a continuous covariate.