We deal with the comparison of space-time covariance kernels having, eit...
The Matérn model has been a cornerstone of spatial statistics for more t...
Covariance functions are the core of spatial statistics, stochastic
proc...
This work provides theoretical foundations for kernel methods in the
hyp...
The advent of data science has provided an increasing number of challeng...
Let d,k be positive integers. We call generalized spaces the cartesian
p...
Characteristic functions that are radially symmetric have a dual
interpr...
The paper deals with multivariate Gaussian random fields defined over
ge...
The Dagum family of isotropic covariance functions has two parameters th...
The Matérn family of isotropic covariance functions has been central to
...
Flexible multivariate covariance models for spatial data are on demand. ...
Cokriging is the common method of spatial interpolation (best linear unb...
We study minimax density estimation on the product space
R^d_1×R^d_2. We...
This paper presents a theoretical analysis of numerical integration base...
Mexico City tracks ground-level ozone levels to assess compliance with
n...
We provide a method for fast and exact simulation of Gaussian random fie...
With the advent of wide-spread global and continental-scale spatiotempor...
We consider the class Ψ_d of continuous functions ψ [0,π]
→R, with ψ(0)=...