Sparse Pseudo-input Local Kriging for Large Spatial Datasets with Exogenous Variables
This addresses computational inefficiency in spatial modeling for applications like environmental science, though it is incremental as it builds on existing Kriging methods.
The paper tackles the challenge of building predictive models for large-scale spatial systems with exogenous variables by proposing Sparse Pseudo-input Local Kriging (SPLK), which partitions domains into subdomains and applies sparse approximations to reduce computational complexity, and numerical experiments show it outperforms or is comparable to common algorithms.
We study large-scale spatial systems that contain exogenous variables, e.g. environmental factors that are significant predictors in spatial processes. Building predictive models for such processes is challenging because the large numbers of observations present makes it inefficient to apply full Kriging. In order to reduce computational complexity, this paper proposes Sparse Pseudo-input Local Kriging (SPLK), which utilizes hyperplanes to partition a domain into smaller subdomains and then applies a sparse approximation of the full Kriging to each subdomain. We also develop an optimization procedure to find the desired hyperplanes. To alleviate the problem of discontinuity in the global predictor, we impose continuity constraints on the boundaries of the neighboring subdomains. Furthermore, partitioning the domain into smaller subdomains makes it possible to use different parameter values for the covariance function in each region and, therefore, the heterogeneity in the data structure can be effectively captured. Numerical experiments demonstrate that SPLK outperforms, or is comparable to, the algorithms commonly applied to spatial datasets.