Maximum‐likelihood estimation for multivariate spatial linear coregionalization models

By Hao Zhang. 2007

99 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): “Other more general algorithms in which all the parameters are estimated simultaneously include simulated annealing [54] and the EM algorithm [114].”
Matérn Cross-Covariance Functions for Multivariate Random Fields (2010, result): “Hence, the assumption of a common scale parameter is not necessarily restrictive, similarly to the reasoning in Zhang and Wang (2010).”
A Valid Matérn Class of Cross-Covariance Functions for Multivariate Random Fields With Any Number of Components (2012, methodology): “A popular approach is to use a linear model of coregionalization (LMC) for stationary random fields (Goulard and Votz 1992; Schmidt and Gelfand 2003; Wackernagel 2003; Gelfand et al. 2004; Zhang 2007).”
Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data (2011, methodology): “Our result is based on the EM algorithm and generalizes Zhang (2007) as it allows for time correlation, covariates and heterotopic data.”
Multi-task Gaussian Process Prediction (2007, background): “A sum of such processes is known as the linear coregionalization model (LCM) [7] for which [6] gives an EM-based algorithm for parameter estimation.”
Spatio-Temporal Covariance and Cross-Covariance Functions of the Great Circle Distance on a Sphere (2016, background): “Environmental data are typically characterized by multivariate measurements at each site of the spatial domain; see, for example, Zhang (2007), Apanasovich andGenton (2010), Furrer and Genton (2011), Kleiber and Genton (2013), and the recent review by Genton and Kleiber (2015).”
Vector Random Fields with Second-Order Moments or Second-Order Increments (2011, background): “However, “only a few multivariate covariogram models have been proposed and used in analyzing real multivariate spatial data” [26], and little progress has been achieved for vector random fields with second-order moments or with secondorder increments, after the fundamental works of Cramér [5] and K”
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals (2013, methodology): “In contrast to existing work extending Gaussian process multi task models by deﬁning more complex covariance structures [2, 9, 8], our model utilizes the gradient of the marginal likelihood for parameter estimation and does not require expected maximization, variational approximation or MCMC…”