Spatio-temporal stationary covariance models

By Chunsheng Ma. 2003

75 citations recorded by the source corpus.

Stationary covariance functions that model space-time interactions are in great demand. The goal of this paper is to introduce and develop new spatio-temporal stationary covanance models. Integral representations for covariance functions with certain properties, such as α-symmetry in the spatial lag, are established. Mixture models are proposed through purely spatial and temporal covariance functions.

Subfields: Economics and Econometrics, Environmental Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e47126940960886da340e412b17a8ea28ec0cf0e

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In Economics and Econometrics

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In Signal Processing

Citations in the corpus

Spatio-temporal variograms and covariance models (2005, methodology): “The random field possesses an orthogonal decomposition (Ma (2003b)) Z(s; t) = Z+(s; t) + Z−(s; t), (s; t) ∈ S × T , Z+(s; t) = 12 {Z(s; t) + Z(−s; −t)}, (s; t) ∈ S × T , Z−(s; t) = 12 {Z(s; t) − Z(−s; −t)}, (s; t) ∈ S × T , where the random fields {Z+(s; t), (s; t) ∈ S × T } and {Z−(s; t), (s; t) ∈…”
Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry (2007, background): “Similar constructions of stationary space-time covariance functions have been reported by Cox and Isham (1988) and Ma (2003b), among others.”
Methods for generating non-separable spatiotemporal covariance models with potential environmental applications (2004, background): “Several theoretical models of non-separable covariances can be found: (a) in the spatiotemporal stochastics literature [2,3,7–15,19,37]; as well as (b) in the statistics literature [16,18,20,27–30,33,36].”