Second-order non-stationary modeling approaches for univariate geostatistical data
F Fouedjio - Stochastic environmental research and risk …, 2017 - Springer
A fundamental decision to make during the analysis of geostatistical data is the modeling of
the spatial dependence structure as stationary or non-stationary. Although second-order …
the spatial dependence structure as stationary or non-stationary. Although second-order …
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 …
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 …
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 …
Posterior inference for sparse hierarchical non-stationary models
Gaussian processes are valuable tools for non-parametric modelling, where typically an
assumption of stationarity is employed. While removing this assumption can improve …
assumption of stationarity is employed. While removing this assumption can improve …
A generalized convolution model and estimation for non-stationary random functions
F Fouedjio, N Desassis, J Rivoirard - Spatial Statistics, 2016 - Elsevier
In this paper, a new model for second order non-stationary random functions as a
convolution of an orthogonal random measure with a spatially varying random weighting …
convolution of an orthogonal random measure with a spatially varying random weighting …
Calibration of SpatioTemporal forecasts from citizen science urban air pollution data with sparse recurrent neural networks
M Bonas, S Castruccio - The Annals of Applied Statistics, 2023 - projecteuclid.org
In this supplement, we present and discuss how well the citizen science PurpleAir sensors
represent the government grade EPA sensors. Additionally, we present descriptions of …
represent the government grade EPA sensors. Additionally, we present descriptions of …
Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field
Estimation of stationary dependence structure parameters using only a single realisation of
the spatial process, typically leads to inaccurate estimates and poorly identified parameters …
the spatial process, typically leads to inaccurate estimates and poorly identified parameters …