Constructing and fitting models for cokriging and multivariable spatial prediction

By Jay M. Ver Hoef, Ronald P. Barry. 1998

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Citations in the corpus

Gaussian Process Regression Networks (2011, background): “…4) the semiparametric latent factor model (SLFM) (Teh et al., 2005), 5) convolved multiple output Gaussian processes (CMOGP) (Barry and Jay, 1996; Ver Hoef and Barry, 1998; Boyle and Frean, 2004), 6) standard independent Gaussian processes (GP), 7) and the DTC (Csat´o and Opper, 2001; Seeger et…”
Some topics in convolution-based spatial modeling (2007, unknown): “Wikle (2002) and Xu et al.”
Space and Space-Time Modeling using Process Convolutions (2002, methodology): “The duality between the moving average process and a stationary Gaussian process determined by its variogram is explored in more detail in Thiebaux and Pedder (1985, ch. 5) and Barry and VerHoef (1996).”
A dynamic process convolution approach to modeling ambient particulate matter concentrations (2008, unknown): “Our approach is similar to the constructive approach for specifying multivariate spatial models proposed in Ver Hoef and Barry (1998) of convolving dependent underlying processes and to the dynamic factor process convolution models for multivariate space-time data discussed in Calder (2007).”
A Valid Matérn Class of Cross-Covariance Functions for Multivariate Random Fields With Any Number of Components (2012, methodology): “Other approaches to valid cross-covariance models include kernel convolution (Ver Hoef and Barry 1998 ) and covariance convolution (Gaspari and Cohn 1999 ; Majumdar and Gelfand 2007 ) methods, both of which generally require numerical integration.”
Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. (2009, background): “However, such models are notoriously difficult to fit; Ver Hoef and Barry (1998) outline some of the difficulties.”
Matérn Cross-Covariance Functions for Multivariate Random Fields (2010, background): “The convolution approach to the construction of multivariate covariance functions has also been studied by Ver Hoef and Barry (1998), Gaspari and Cohn (1999), Oliver (2003), and Ver Hoef, Cressie, and Barry (2004), among other authors.”
Computationally Efficient Convolved Multiple Output Gaussian Processes (2011, methodology): “(10) The covariance between two different outputsyd(x) andyd′(x′) is then recovered as cov [ yd(x),yd′(x ′) ] =cov [ fd(x),fd′(x ′) ] +cov [ wd(x),wd′(x ′) ] δd,d′ , whereδd,d′ is the Kronecker delta function.5 As mentioned before, Ver Hoef and Barry (1998) and Higdon (2002) proposed the direct use ”
Classes of compactly supported covariance functions for multivariate random fields (2015, background): “2013) as well as earlier papers (Wackernagel 2003; Gaspari and Cohn 1999; Hoef and Barry 1998; Goulard and Voltz 1992) confirm that the construction of models for matrix-valued covariances is of real interest to the geostatistical community.”
Interpolation of nonstationary air pollution processes: a spatial spectral approach (2002, methodology): “Ver Hoef and Barry (1998) and Higdon et al. (1999) give a model for accounting for heterogeneity in the spatial covariance function of a spatial process, using a moving average speci cation of a Gaussian process.”
Flexible Spatial Models for Kriging and Cokriging Using Moving Averages and the Fast Fourier Transform (FFT) (2004, methodology): “More details were given by Ver Hoef and Barry (1998); see also Oliver (1995).”