Modelling spatio-temporal variation in sparse rainfall data using a hierarchical Bayesian regression model

DOI10.1007/s13253-019-00357-3zbMath1426.62353OpenAlexW2940545346WikidataQ128162970 ScholiaQ128162970MaRDI QIDQ2419852

Hans-Peter Piepho, Sabyasachi Mukhopadhyay, Holly T. Dublin, Gundula Bartzke, Joseph O. Ogutu

Publication date: 4 June 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/s13253-019-00357-3

zbMATH Keywords

Gaussian processMCMCMaasai Mara ecosystemnon-stationary covariance function

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Geostatistics (86A32)

Uses Software

• fields
• spTimer

Cites Work

• Unnamed Item
• Estimation of Relationships for Limited Dependent Variables
• A comparison of Bayesian models for daily ozone concentration levels
• Penalising model component complexity: a principled, practical approach to constructing priors
• A Latent Gaussian Markov Random-Field Model for Spatiotemporal Rainfall Disaggregation
• Gaussian Predictive Process Models for Large Spatial Data Sets
• High-Resolution Space–Time Ozone Modeling for Assessing Trends
• Venezuelan Rainfall Data Analysed by Using a Bayesian Space–time Model
• A Comparison of Spatio-Temporal Bayesian Models for Reconstruction of Rainfall Fields in a Cloud Seeding Experiment
• Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics

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