covatest: An R Package for Selecting a Class of Space-Time Covariance Functions

被引：15

作者：

Cappello, Claudia ^{[1
]}

De Iaco, Sandra ^{[1
]}

Posa, Donato ^{[1
]}

机构：

[1] Univ Salento, Dept Management Econ Math & Stat, I-73100 Lecce, Italy

来源：

JOURNAL OF STATISTICAL SOFTWARE | 2020年 / 94卷 / 01期

关键词：

space-time covariance functions; symmetry; separability; type of non-separability; test on classes of space-time covariance functions; STRICT POSITIVE DEFINITENESS; RANDOM-FIELDS; FORTRAN PROGRAMS; MODELS; NONSEPARABILITY; PREDICTION; PRODUCT; GSTAT;

D O I：

10.18637/jss.v094.i01

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Although a very rich list of classes of space-time covariance functions exists, specific tools for selecting the appropriate class for a given data set are needed. Thus, the main topic of this paper is to present the new R package, covatest, which can be used for testing some characteristics of a covariance function, such as symmetry, separability and type of non-separability, as well as for testing the adequacy of some classes of space-time covariance models. These last aspects can be relevant for choosing a suitable class of covariance models. The proposed results have been applied to an environmental case study.

引用

页码：1 / 42

页数：42

共 33 条

[21] Nonseparable space-time covariance models: Some parametric families
De Iaco, S
Myers, DE
Posa, D
MATHEMATICAL GEOLOGY, 2002, 34 (01): : 23 - 42
[22] Nonseparable Space-Time Covariance Models: Some Parametric Families
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D. E. Myers
D. Posa
Mathematical Geology, 2002, 34 : 23 - 42
[23] Nonseparable stationary anisotropic space–time covariance functions
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P. Gregori
J. Mateu
Stochastic Environmental Research and Risk Assessment, 2006, 21 : 113 - 122
[24] Covariance functions that are stationary or nonstationary in space and stationary in time
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Mateu, J.
STATISTICA NEERLANDICA, 2007, 61 (03) : 358 - 382
[25] Positive and negative non-separability for space-time covariance models
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[26] The reverse dimple in potentially negative-value space-time covariance models
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[27] Local Likelihood Estimation for Covariance Functions with Spatially-Varying Parameters: The convoSPAT Package for R
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[28] Kernel-Based Reconstruction of Space-Time Functions on Dynamic Graphs
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[29] Testing the type of non-separability and some classes of space-time covariance function models
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Stochastic Environmental Research and Risk Assessment, 2018, 32 : 17 - 35
[30] Linking space-time correlations for a class of self-organized critical systems
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PHYSICAL REVIEW E, 2021, 104 (06)

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