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1.
Emery  Xavier  Porcu  Emilio  White  Philip 《Mathematical Geosciences》2022,54(6):1043-1068
Mathematical Geosciences - This paper addresses the problem of finding parametric constraints that ensure the validity of the multivariate Matérn covariance for modeling the spatial...  相似文献   
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Quasi arithmetic and Archimedean functionals are used to build new classes of spectral densities for processes defined on any d-dimensional lattice \mathbbZd{\mathbb{Z}^d} and random fields defined on the d-dimensional Euclidean space \mathbbRd{\mathbb{R}^d}, given simple margins. We discuss the mathematical features of the proposed constructions, and show rigorously as well as through examples, that these new classes of spectra generalize celebrated classes introduced in the literature. Additionally, we obtain permissible spectral densities as linear combinations of quasi arithmetic or Archimedean functionals, whose associated correlation functions may attain negative values or oscillate between positive and negative ones. We finally show that these new classes of spectral densities can be used for nonseparable processes that are not necessarily diagonally symmetric.  相似文献   
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We propose a spectral turning-bands approach for the simulation of second-order stationary vector Gaussian random fields. The approach improves existing spectral methods through coupling with importance sampling techniques. A notable insight is that one can simulate any vector random field whose direct and cross-covariance functions are continuous and absolutely integrable, provided that one knows the analytical expression of their spectral densities, without the need for these spectral densities to have a bounded support. The simulation algorithm is computationally faster than circulant-embedding techniques, lends itself to parallel computing and has a low memory storage requirement. Numerical examples with varied spatial correlation structures are presented to demonstrate the accuracy and versatility of the proposal.  相似文献   
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Stochastic Environmental Research and Risk Assessment - Axial symmetry for covariance functions defined over spheres has been a very popular assumption for climate, atmospheric, and environmental...  相似文献   
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There is a great demand for statistical modeling of phenomena that evolve in both space and time, and thus, there is a growing literature on correlation function models for spatio-temporal processes. In particular, various properties of these correlation functions have been studied only for the merely spatial or temporal case, fact that constitutes a strong motivation for our work. The goal of this paper is to inspect some properties, obtained with respect to partial differentiation and integration, of stationary spatio-temporal correlation functions for which anisotropy is obtained through isotropy between components as in Fernández-Casal et al. (Stat Comput 13(2):127–136, 2003). We show that through partial differentiation and integration it is possible to obtain permissible spatio-temporal correlation functions in the space–time domain. Other new results regard specific classes of space–time correlations introduced in recent literature. A curious result arises by differentiating scale mixtures of Euclid’s hat. Work partially funded by grant MTM2004-06231 from the Spanish Ministery of Science and Education.  相似文献   
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Radial basis functions with compact support for multivariate geostatistics   总被引:3,自引:3,他引:0  
Matrix-valued radially symmetric covariance functions (also called radial basis functions in the numerical analysis literature) are crucial for the analysis, inference and prediction of Gaussian vector-valued random fields. This paper provides different methodologies for the construction of matrix-valued mappings that are positive definite and compactly supported over the sphere of a d-dimensional space, of a given radius. In particular, we offer a representation based on scaled mixtures of Askey functions; we also suggest a method of construction based on B-splines. Finally, we show that the very appealing convolution arguments are indeed effective when working in one dimension, prohibitive in two and feasible, but substantially useless, when working in three dimensions. We exhibit the statistical performance of the proposed models through simulation study and then discuss the computational gains that come from our constructions when the parameters are estimated via maximum likelihood. We finally apply our constructions to a North American Pacific Northwest temperatures dataset.  相似文献   
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Dynamic life tables arise as an alternative to the standard (static) life tables with the aim of incorporating the evolution of mortality over time. These tables can be considered as a two-way table on a grid equally spaced in either the vertical (age) or horizontal (year) directions, and the data can be decomposed into a deterministic large-scale variation (trend) plus a stochastic small-scale variation (residuals). In this context, space–time geostatistical methods can be used for fitting and predicting the dynamic mortality. We use four different space–time covariance functions for fitting and predicting mortality in Spain during the period 1980–2005. In particular, we aim at showing the behavior of separable versus nonseparable fitted structures on one hand, and the behavior of simple structures given by combinations of products and sums versus more complicated negative structures on the other hand.  相似文献   
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