30 Years of space–time covariance functions
In this article, we provide a comprehensive review of space–time covariance functions. As
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …
A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2. 5 concentration
A typical challenge in air pollution epidemiology is to perform detailed exposure assessment
for individuals for which health data are available. To address this problem, in the last few …
for individuals for which health data are available. To address this problem, in the last few …
Spatial models with explanatory variables in the dependence structure
R Ingebrigtsen, F Lindgren, I Steinsland - Spatial Statistics, 2014 - Elsevier
Geostatistical models have traditionally been stationary. However, physical knowledge
about underlying spatial processes often requires models with non-stationary dependence …
about underlying spatial processes often requires models with non-stationary dependence …
Non-stationary dependence structures for spatial extremes
Max-stable processes are natural models for spatial extremes because they provide suitable
asymptotic approximations to the distribution of maxima of random fields. In the recent past …
asymptotic approximations to the distribution of maxima of random fields. In the recent past …
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 …
Covariance structure of spatial and spatiotemporal processes
P Guttorp, AM Schmidt - Wiley Interdisciplinary Reviews …, 2013 - Wiley Online Library
An important aspect of statistical modeling of spatial or spatiotemporal data is to determine
the covariance function. It is a key part of spatial prediction (kriging). The classical …
the covariance function. It is a key part of spatial prediction (kriging). The classical …
Conditional formulation for the calibration of multi-level random fields with incomplete data
Random fields are widely used in the modeling of spatially varying quantities. As an
extension, multi-level random fields capture not only the spatial variability, but also the …
extension, multi-level random fields capture not only the spatial variability, but also the …
Dynamic spatio-temporal models for spatial data
Analyzing spatial data often requires modeling dependencies created by a dynamic spatio-
temporal data generating process. In many applications, a generalized linear mixed model …
temporal data generating process. In many applications, a generalized linear mixed model …
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 of complex tail dependence structures, applied to US precipitation extremes
D Castro-Camilo, R Huser - Journal of the American Statistical …, 2020 - Taylor & Francis
To disentangle the complex nonstationary dependence structure of precipitation extremes
over the entire contiguous United States (US), we propose a flexible local approach based …
over the entire contiguous United States (US), we propose a flexible local approach based …