Separable approximations of space‐time covariance matrices

By Marc G. Genton. 2007

193 citations recorded by the source corpus.

Subfields: Ecology, Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/74eef2391c39e5679f7595ce2ac5648adf2fe43b

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Citations in the corpus

Efficient Emulators for Multivariate Deterministic Functions (2008, methodology): “A similar approach has been proposed when the outputs are not regular or κ(·) is not separable, which is to replace the original W matrix with an approximate Kronecker product decomposition (Genton 2007).”
Detection of change in the spatiotemporal mean function (2017, background): “see Haas (1995), Genton (2007), Hoff (2011) and Paul and Peng (2011).”
Tests for separability in nonparametric covariance operators of random surfaces (2015, background): “, Genton (2007), Gneiting, Genton and Guttorp (2007)] simplifies the covariance structure of the process and makes it far easier to estimate; in some sense, the separability assumption results in a estimator of the covariance which has less variance, at the expense of a possible bias.”
Joint composite estimating functions in spatiotemporal models (2012, methodology): “Comprehensive simulation studies have shown that the JCEF is advantageous over MLE, tapering, Stein’s method, WCL and WLS in terms of balancing estimation and computational efficiency for large data sets. Another advantage of the JCEF method is the possibility of deriving a goodness-of-fit statistic”
Spiked separable covariance matrices and principal components (2019, methodology): “of Irish wind speed [24]), the covariance matrix can be well approximated using separable covariance matrices by solving a nearest Kronecker product for a space-time covariance matrix problem (NKPST) [23]. Consider a pˆndata matrix Y of the form Y “ Ar1{2XBr1{2, (1.1) where X“ pxijq is a pˆnrandom m”
Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes (2019, background): “In many instances, it is assumed that the covariance of X is separable , meaning that there exists a p × p covariance matrix ∆ and covariance operator B on L 2 [0 , 1] such that G = ∆ B (Gneiting et al. 2006, Genton 2007, Aston et al. 2017).”
Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity (2020, background): “…(Angulo et al., 2013; Christakos & Hristopulos, 1998; Fei et al., 2019; He et al., 2019), meteorology, environmental monitoring, and design (e.g., Genton, 2007; Jona‐ Lasinio et al., 2012; Kleiber, 2016; Li et al., 2008; Nevat et al., 2015; Sampson & Guttorp, 1992; Youngman & Stephenson, 2016),…”