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• W2592897948 abstract "With the development of new remote sensing technology, large or even massive spatial datasets covering the globe becomes available. Statistical analysis of such data is challenging. This article proposes a semiparametric approach to model large or massive spatial datasets. In particular, a Gaussian process with additive components is proposed, with its covariance structure consisting of two components: one component is flexible without assuming a specific parametric covariance function but is able to achieve dimension reduction; the other is parametric and simultaneously induces sparsity. The inference algorithm for parameter estimation and spatial prediction is devised. The resulting spatial prediction method that we call fused Gaussian process (FGP), is applied to simulated data and a massive satellite dataset. The results demonstrate the computational and inferential benefits of the FGP over competing methods and show that the FGP is more flexible and robust against model misspecification." @default.
• W2592897948 created "2017-03-16" @default.
• W2592897948 creator A5043233797 @default.
• W2592897948 creator A5054766234 @default.
• W2592897948 date "2017-02-28" @default.
• W2592897948 modified "2023-09-27" @default.
• W2592897948 title "Fused Gaussian Process for Very Large Spatial Data" @default.
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