Modeling and prediction of multivariate space-time random fields

DOI10.1016/j.csda.2004.02.011zbMath1429.62426OpenAlexW2115605389MaRDI QIDQ957133

D. Posa, S. De Iaco, G. Massimo Palma

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2004.02.011

zbMATH Keywords

space-time linear coregionalization modelmultivariate space-time random fieldspace-time prediction

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Geostatistics (86A32)

Related Items (6)

Prediction of particle pollution through spatio-temporal multivariate geostatistical analysis: spatial special issue ⋮ Prediction for spatio-temporal models with autoregression in errors ⋮ Space–time correlation analysis: a comparative study ⋮ A new space–time multivariate approach for environmental data analysis ⋮ Using simultaneous diagonalization to identify a space-time linear coregionalization model ⋮ Tucker tensor analysis of Matérn functions in spatial statistics

Uses Software

• GSLIB

Cites Work

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• The linear coregionalization model and the product-sum space-time variogram
• Time series: theory and methods
• Space-time variograms and a functional form for total air pollution measurements.
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
• Space-time analysis using a general product-sum model.
• Geostatistical space-time models: a review

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