Product‐sum covariance for space‐time modeling: an environmental application

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Estimating and modeling space–time correlation structures (2001, background): “It is assumed (Dimitrakopoulos and Lou, 1993) that: Cst(hs;ht) = C(a2jhsj2 + b2h2t) ( 5 )”
On potentially negative space time covariances obtained as sum of products of marginal ones (2008, methodology): “However we are not considering real data analysis in this paper because of the following reasons: (a) analysing this kind of data means entering into a wide problem, the problem of estimation, and in particular the problem of approximate likelihood methods that allow for avoiding computational burde”
Nonseparable, Stationary Covariance Functions for Space–Time Data (2002, background): “…might well occur in other geophysical or environmental data sets, such as wind speeds over the tropical western Paci c Ocean, as analyzed by Cressie and Huang (1999), or atmospheric pollutant concentrations in the Milan district, Italy, as recently modeled by De Cesare, Myers, and Posa (2001).”
New classes of covariance and spectral density functions for spatio-temporal modelling (2008, background): “It is worth citating Jones and Zhang (1997), Cressie and Huang (1999), Christakos (2000), De Cesare et al. (2001) , Gneiting (2002), Ma (2003), Stein (2005) and Ferna ´ndez-Casal (2003).”