Regression‐based covariance functions for nonstationary spatial modeling
In many environmental applications involving spatially‐referenced data, limitations on the
number and locations of observations motivate the need for practical and efficient models for …
number and locations of observations motivate the need for practical and efficient models for …
Nonstationary spatial modeling, with emphasis on process convolution and covariate-driven approaches
MD Risser - arXiv preprint arXiv:1610.02447, 2016 - arxiv.org
In many environmental applications involving spatially-referenced data, limitations on the
number and locations of observations motivate the need for practical and efficient models for …
number and locations of observations motivate the need for practical and efficient models for …
Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R
In spite of the interest in and appeal of convolution-based approaches for nonstationary
spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution …
spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution …
[HTML][HTML] Nonstationary modeling for multivariate spatial processes
We derive a class of matrix valued covariance functions where the direct and cross-
covariance functions are Matérn. The parameters of the Matérn class are allowed to vary …
covariance functions are Matérn. The parameters of the Matérn class are allowed to vary …
Estimation and prediction of a class of convolution-based spatial nonstationary models for large spatial data
Z Zhu, Y Wu - Journal of Computational and Graphical Statistics, 2010 - Taylor & Francis
In this article we address two important issues common to the analysis of large spatial
datasets. One is the modeling of nonstationarity, and the other is the computational …
datasets. One is the modeling of nonstationarity, and the other is the computational …
[PDF][PDF] Some topics in convolution-based spatial modeling
Over the last decade, convolution-based models for spatial data have increased in
popularity as a result of their flexibility in modeling spatial dependence and their ability to …
popularity as a result of their flexibility in modeling spatial dependence and their ability to …
Spatial modelling using a new class of nonstationary covariance functions
CJ Paciorek, MJ Schervish - Environmetrics: The official journal …, 2006 - Wiley Online Library
We introduce a new class of nonstationary covariance functions for spatial modelling.
Nonstationary covariance functions allow the model to adapt to spatial surfaces whose …
Nonstationary covariance functions allow the model to adapt to spatial surfaces whose …
Efficient estimation of nonstationary spatial covariance functions with application to high-resolution climate model emulation
Spatial processes exhibit nonstationarity in many climate and environmental applications.
Convolution-based approaches are often used to construct nonstationary covariance …
Convolution-based approaches are often used to construct nonstationary covariance …
Semiparametric estimation and selection for nonstationary spatial covariance functions
We propose a method for estimating nonstationary spatial covariance functions by
representing a spatial process as a linear combination of some local basis functions with …
representing a spatial process as a linear combination of some local basis functions with …
Does non-stationary spatial data always require non-stationary random fields?
A stationary spatial model is an idealization and we expect that the true dependence
structures of physical phenomena are spatially varying, but how should we handle this non …
structures of physical phenomena are spatially varying, but how should we handle this non …