Modeling temporally evolving and spatially globally dependent data
The last decades have seen an unprecedented increase in the availability of data sets that
are inherently global and temporally evolving, from remotely sensed networks to climate …
are inherently global and temporally evolving, from remotely sensed networks to climate …
Inference for gaussian processes with matérn covariogram on compact riemannian manifolds
Gaussian processes are widely employed as versatile modelling and predictive tools in
spatial statistics, functional data analysis, computer modelling and diverse applications of …
spatial statistics, functional data analysis, computer modelling and diverse applications of …
Axially symmetric models for global data: a journey between geostatistics and stochastic generators
Decades of research in spatial statistics have prompted the development of a wide variety of
models and methods whose primary goal is optimal linear interpolation (kriging), as well as …
models and methods whose primary goal is optimal linear interpolation (kriging), as well as …
The F-family of covariance functions: A Matérn analogue for modeling random fields on spheres
The Matérn family of isotropic covariance functions has been central to the theoretical
development and application of statistical models for geospatial data. For global data …
development and application of statistical models for geospatial data. For global data …
Necessary and sufficient conditions for asymptotically optimal linear prediction of random fields on compact metric spaces
K Kirchner, D Bolin - The Annals of Statistics, 2022 - projecteuclid.org
Necessary and sufficient conditions for asymptotically optimal linear prediction of random
fields on compact metric spaces Page 1 The Annals of Statistics 2022, Vol. 50, No. 2, 1038–1065 …
fields on compact metric spaces Page 1 The Annals of Statistics 2022, Vol. 50, No. 2, 1038–1065 …
Admissible nested covariance models over spheres cross time
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 …
are very popular in many branches of applied statistics, and in particular in geostatistics. A …
Generalised Wendland functions for the sphere
In this paper, we compute the spherical Fourier expansion coefficients for the restriction of
the generalised Wendland functions from d-dimensional Euclidean space to the (d− 1) …
the generalised Wendland functions from d-dimensional Euclidean space to the (d− 1) …
[PDF][PDF] A family of covariance functions for random fields on spheres
The Matérn family of isotropic covariance functions has been central to the theoretical
development and application of statistical models for geospatial data. For global data …
development and application of statistical models for geospatial data. For global data …
Fixed-domain posterior contraction rates for spatial Gaussian process model with nugget
Spatial Gaussian process regression models typically contain finite dimensional covariance
parameters that need to be estimated from the data. We study the Bayesian estimation of …
parameters that need to be estimated from the data. We study the Bayesian estimation of …
Multivariate Gaussian random fields over generalized product spaces involving the hypertorus
The paper deals with multivariate Gaussian random fields defined over generalized product
spaces that involve the hypertorus. The assumption of Gaussianity implies the finite …
spaces that involve the hypertorus. The assumption of Gaussianity implies the finite …