Admissible nested covariance models over spheres cross time
- 156 Downloads
Nested covariance models, defined as linear combinations of basic covariance functions, are very popular in many branches of applied statistics, and in particular in geostatistics. A notorious limit of nested models is that the constants in the linear combination are bound to be nonnegative in order to preserve positive definiteness (admissibility). This paper studies nested models on d-dimensional spheres and spheres cross time. We show the exact interval of admissibility for the constants involved in the linear combinations. In particular, we show that at least one constant can be negative. One of the implications is that one can obtain a nested model attaining negative correlations. We provide characterization theorems for arbitrary linear combinations as well as for nonconvex combinations involving two covariance functions. We illustrate our findings through several examples involving nonconvex combinations of well-known parametric families of covariance functions.
KeywordsCovariance functions Nested models Negative covariance Spheres
Ana Peron was partially supported by São Paulo Research Foundation (FAPESP) under Grants 2016/03015-7 and 2016/09906-0. Emilio Porcu and Xavier Emery acknowledge the support of Grant CONICYT/FONDECYT/REGULAR/1170290 from the Chilean Commission for Scientific and Technological Research.
- Clarke J, Alegría A, Porcu E (2018) Regularity properties and simulations of Gaussian random fields on the sphere cross time. Electron J Stat 12:399–426. arXiv:1611.02851
- Dai F, Xu Y (2013) Approximation theory and harmonic analysis on spheres and balls. Springer monographs in mathematics. Springer, New YorkGoogle Scholar
- Estrade A, Fariñas A, Porcu E (2017) Characterization theorems for covariance functions on the n-dimensional sphere across time. Technical report, University Federico Santa Maria, MAP5 2016-34 [hal-01417668]Google Scholar
- Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, CambridgeGoogle Scholar
- Porcu E, Alegria A, Furrer R (2017) Modeling temporally evolving and spatially globally dependent data. Int Stat Rev. arXiv:1706.09233