Journal ArticleDOI
Families of spatio-temporal stationary covariance models
TLDR
In this paper, the authors provide simple methods for constructing new families of spatio-temporal stationary covariance models from purely spatial (or purely temporal) stationary models, including the Heine family and the Whittle-Matern family.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 2003-10-01. It has received 126 citations till now. The article focuses on the topics: Covariance function & Matérn covariance function.read more
Citations
More filters
Journal ArticleDOI
Space–Time Covariance Functions
TL;DR: In this paper, the authors consider a number of properties of space-time covariance functions and how these relate to the spatial-temporal interactions of the process and obtain a parametric class of spectral densities whose corresponding space time covariance function are infinitely differentiable away from the origin and allow for essentially arbitrary and possibly different degrees of smoothness for the process in space and time.
Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry
TL;DR: In this paper, the authors review recent advances in the literature on space-time covariance functions in light of the aforementioned notions, which are illustrated using wind data from Ireland, and suggest that the use of more complex and more realistic covariance models results in improved predictive performance.
Journal ArticleDOI
Spatial and spatio-temporal Log-Gaussian Cox processes:extending the geostatistical paradigm
TL;DR: This paper first describes the class of log-Gaussian Cox processes (LGCPs) as models for spatial and spatio-temporal point process data, and discusses inference, with a particular focus on the computational challenges of likelihood-based inference.
Journal ArticleDOI
A general science-based framework for dynamical spatio-temporal models
TL;DR: In this article, the authors present a framework for incorporating scientific information to motivate dynamical spatio-temporal models and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases.
Journal ArticleDOI
Separable approximations of space‐time covariance matrices
Marc G. Genton,Marc G. Genton +1 more
TL;DR: In this paper, the authors discuss separable approximations of non-separable space-time covariance matrices, that is, covariances that can be written as a product of a purely spatial covariance and a purely temporal covariance.
References
More filters
Journal ArticleDOI
5. Statistics for Spatial Data
Mike Rees,N. Cressie +1 more
TL;DR: Cressie et al. as discussed by the authors presented the Statistics for Spatial Data (SDS) for the first time in 1991, and used it for the purpose of statistical analysis of spatial data.
Book
Geostatistics: Modeling Spatial Uncertainty
Jean-Paul Chilès,Pierre Delfiner +1 more
TL;DR: In this article, the Intrinsic Model of Order (IMO) is used for structural analysis and nonlinear methods are used for nonlinear models of scale effects and inverse problems.
Book
Interpolation of Spatial Data: Some Theory for Kriging
TL;DR: This chapter discusses the role of asymptotics for BLPs, and applications of equivalence and orthogonality of Gaussian measures to linear prediction, and the importance of Observations not part of a sequence.
Journal ArticleDOI
On stationary processes in the plane
TL;DR: The sampling theory of stationary processes in space is not completely analogous to that of stationary time series, due to the fact that the variate of a time series is influenced only by past values, while for a spatial process dependence extends in all directions as mentioned in this paper.