Spectral methods for nonstationary spatial processes

By Montserrat Fuentes. 2002

285 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering

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Citations in the corpus

Kernels for Vector-Valued Functions: a Review (2011, methodology): “Some of those extensions include the construction of nonstationary covariances [43, 45, 30, 31, 73] and spatiotemporal covariances [109, 107, 108].”
Nonstationary covariance models for global data (2008, methodology): “Kernel convolution and its variants have been applied in several papers to create nonstationary covariance functions such as in Higdon (1998), Higdon, Swall and Kern (1999), Fuentes (2002) and Paciorek and Schervish (2006).”
Some topics in convolution-based spatial modeling (2007, methodology): “Since it is based on convolutions of dependent processes, this approach has asimil r flavor to the Fuentes approach to modeling nonstationary spatial processes (Fuentes, 2002a,b).”
Second-order non-stationary modeling approaches for univariate geostatistical data (2017, methodology): “Gosoniu and Vounatsou (2011) apply the model proposed by Fuentes (2001) to malaria prevalence data in West Africa.”
Space–Time Covariance Functions (2005, methodology): “This section applies some of the models described here to the Irish wind data studied by Haslett and Raftery (1989), Gneiting (2002), and de Luna and Genton (2003).”
Nonstationary spatial covariance functions (2005, methodology): “Higdon (1998) and Fuentes (2002) describe methods for generating nonstationary covariance functions as integrals, but these integrals generally have to be carried out numerically.”
Spatial statistical models that use flow and stream distance (2006, methodology): “There is increasing use of these moving average models to construct valid autocovariances when confronted with new problems, such as multivariate models (Ver Hoef and Barry 1998; Ver Hoef et al. 2004), and nonstationary models (Higdon 1998; Higdon et al. 1999; Fuentes 2002).”
New classes of covariance and spectral density functions for spatio-temporal modelling (2008, background): “In the nonstationary context, important contributions come from Christakos (2000), Fuentes and Smith (2001), Fuentes (2002) , Christakos (2002) and Kolovos et al. (2004).”
A Moving Average Approach for Spatial Statistical Models of Stream Networks (2010, methodology): “Moving average models are being increasingly used to construct valid autocovariances when confronted with new problems, such as flexible, nonparametric models (Barry and Ver Hoef 1996), multivariate models (Ver Hoef and Barry 1998; Ver Hoef, Cressie, and Barry 2004), and nonstationary models (Higdon”
Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field (2014, methodology): “, 1999; Paciorek and Schervish, 2006), or with a spatial dependent process (Fuentes, 2002), to obtain non-stationarity.”
Spatial models with explanatory variables in the dependence structure (2014, methodology): “Another popular approach has been kernel convolution methods (e.g. Higdon et al., 1999; Fuentes, 2002; Paciorek and Schervish, 2006; Reich et al., 2011; Kleiber and Nychka, 2012).”
Generalized Spatial and Spatiotemporal Autoregressive Conditional Heteroscedasticity (2016, background): “Thus, Sampson & Guttorp (1992), Fuentes (2001), Fuentes (2002), and Schmidt & O’Hagan (2003), among others, have introduced various approaches to treat nonstationary spatial processes.”
A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature (2009, methodology): “This was done by considering processes with either spatially varying covariance parameters (Fuentes 2002) or spatially varying convolving kernels (Higdon, Swall, and Kern 1999).”
Non-Gaussian Bayesian Geostatistical Modeling (2006, methodology): “In addition, we could use a convolution of GLG processes rather than Gaussian processes to induce non-stationarity in an approach similar to Fuentes (2002) .”
A formal test for nonstationarity of spatial stochastic processes (2005, methodology): “Fuentes [11] introduced a likelihood-based approach to estimate the local stationary dependency structure of a nonstationary process.”
Trend Filtering Methods for Momentum Strategies (2011, background): “But the main drawback of spectral analysis is that it is not well suited to nonstationary processes (Martin and Flandrin, 1985, Fuentes, 2002, Oppenheim and Schafer, 2009).”
A kernel-based spectral model for non-Gaussian spatio-temporal processes (2002, background): “For illustration, consider the one-dimensional Gaussian spatial kernel, ks…r; y1 ; y2† ˆ 1 y2 2p p expf¡0:5…r ¡ y1 ¡ s†2=y2g …3:2† where the kernel is centered at y1 ‡ s and thus is shifted by y1 spatial units relative to location s, and y2 is the scale parameter.”
Testing for separability of spatial–temporal covariance functions (2006, background): “However, such assumptions might not hold in large heterogeneous fields ( Fuentes, 2001, 2002 ).”