A mixture model approach for compositional data: inferring land-use influence on point-referenced water quality measurements

DOI10.1007/S13253-019-00371-5zbMath1428.62489OpenAlexW2963014085WikidataQ127491101 ScholiaQ127491101MaRDI QIDQ2009146

Adrien Ickowicz, Jessica Ford, Keith Hayes

Publication date: 27 November 2019

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-019-00371-5

zbMATH Keywords

Bayesian analysismodel-based clusteringspatio-temporal datachange of support problem

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Uses Software

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• CRAN
• GMRFLib
• Rcpp
• BayesDA
• SpatioTemporal
• CAPUSHE
• spTimer
• PRMLT

Cites Work

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• Unnamed Item
• An external field prior for the hidden Potts model with application to cone-beam computed tomography
• Model-based clustering and segmentation of time series with changes in regime
• Slope heuristics: overview and implementation
• Inference from iterative simulation using multiple sequences
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• On the change of support problem for spatio-temporal data
• Gaussian Markov Random Fields
• Bayes Factors
• Variable Selection for Model-Based Clustering
• Spatial clustering of time series via mixture of autoregressions models and Markov random fields

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