A Fused Gaussian Process Model for Very Large Spatial Data. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Ma, Pulong; Kang, Emily L.
• Is Part Of: Journal of computational and graphical statistics. Volume 29:Issue 3(2020)
• Page Start: 479
• Page End: 489
• Publication Date: 2020-07-02
• Subjects: Basis function -- Dimension reduction -- Fused Gaussian process -- Gaussian graphical model -- Semiparametric covariance; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2019.1704293
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become 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 methodology 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 FGP over competing methods and show that FGP is robust against model misspecification and captures spatial nonstationarity. Supplementary materials for this article are available online.