Interpreting self-organizing maps through space-time data models

DOI10.1214/08-AOAS174zbMath1454.62457arXiv0901.3494OpenAlexW2113568568WikidataQ57516071 ScholiaQ57516071MaRDI QIDQ999649

Alan E. Gelfand, Chris Lennard, Gabriele Hegerl, Huiyan Sang, Bruce Hewitson

Publication date: 10 February 2009

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0901.3494

zbMATH Keywords

Markov chain Monte Carlomodel choicespace-time modelsvector autoregressive modelbivariate spatial predictive process

Mathematics Subject Classification ID

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

Related Items (1)

Special section on statistics in the atmospheric sciences

Uses Software

• StFinMetrics

Cites Work

• Unnamed Item
• Bayesian analysis of vector-autoregressive models with noninformative priors.
• Nonstationary multivariate process modeling through spatially varying coregionalization
• Modeling Financial Time Series with S-PLUS®
• Gaussian Predictive Process Models for Large Spatial Data Sets
• A Non-Homogeneous Hidden Markov Model for Precipitation Occurrence
• Bayesian Measures of Model Complexity and Fit

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