Multivariate spatial meta kriging

Mapping Spatial Thematic Accuracy Using Indicator Kriging

Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations becomes large. There is a burgeoning literature on approaches for analyzing large spatial datasets. In this article, we propose a divide-and-conquer strategy within the Bayesian paradigm. We partition the data into subsets, analyze each subset using a Bayesian ...

Part 4: Spatial Prediction and Kriging

where the covariance structure of U(s) is unknown. The prediction of U(s) at a new spatial site s0 is known as kriging (though the term has been mainly used for the construction of a spatial predictor using a model with known parameters). A generalization is to predict the joint value at several points, or an integral such as u(A) = ∫ A u(s) ds doe some set A (commonly of interest in areal data...

Multivariate versus univariate Kriging metamodels for multi-response simulation models

To analyze the input/output behavior of simulation models with multiple responses, we may apply either univariate or multivariate Kriging (Gaussian process) metamodels. In multivariate Kriging we face a major problem: the covariance matrix of all responses should remain positive-definite; we therefore use the recently proposed “nonseparable dependence” model. To evaluate the performance of univ...

Parallel Multivariate Meta-Theorems

Fixed-parameter tractability is based on the observation that many hard problems become tractable even on large inputs as long as certain input parameters are small. Originally, “tractable” just meant “solvable in polynomial time,” but especially modern hardware raises the question of whether we can also achieve “solvable in polylogarithmic parallel time.” A framework for this study of parallel...