New Flexible Compact Covariance Model on a Sphere

2017/01/12 by Alexander Gribov, Gribov, Alexander, Konstantin Krivoruchko +1
Economics, Econometrics and Finance · Environmental Science · #Computation (stat.CO) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Soil Geostatistics and Mapping #Spatial and Panel Data Analysis

paper · pdf · doi:10.48550/arxiv.1701.03405

openalex publication_date 2017/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We discuss how the kernel convolution approach can be used to accurately approximate the spatial covariance model on a sphere using spherical distances between points. A detailed derivation of the required formulas is provided. The proposed covariance model approximation can be used for non-stationary spatial prediction and simulation in the case when the dataset is large and the covariance model can be estimated separately in the data subsets.

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