DeepAI
Log In Sign Up

Locally anisotropic covariance functions on the sphere

08/15/2022
by   Jian Cao, et al.
0

Rapid developments in satellite remote-sensing technology have enabled the collection of geospatial data on a global scale, hence increasing the need for covariance functions that can capture spatial dependence on spherical domains. We propose a general method of constructing nonstationary, locally anisotropic covariance functions on the sphere based on covariance functions in R^3. We also provide theorems that specify the conditions under which the resulting correlation function is isotropic or axially symmetric. For large datasets on the sphere commonly seen in modern applications, the Vecchia approximation is used to achieve higher scalability on statistical inference. The importance of flexible covariance structures is demonstrated numerically using simulated data and a precipitation dataset.

READ FULL TEXT

page 9

page 11

12/21/2018

Structured space-sphere point processes and K-functions

This paper concerns space-sphere point processes, that is, point process...
11/11/2017

A Test for Isotropy on a Sphere using Spherical Harmonic Functions

Analysis of geostatistical data is often based on the assumption that th...
06/30/2020

Spatiotemporal Multi-Resolution Approximations for Analyzing Global Environmental Data

Technological developments and open data policies have made large, globa...
06/03/2021

Slepian Scale-Discretised Wavelets on the Sphere

This work presents the construction of a novel spherical wavelet basis d...
01/09/2020

Rapid Numerical Approximation Method for Integrated Covariance Functions Over Irregular Data Regions

In many practical applications, spatial data are often collected at area...
06/19/2021

Discussion on Competition for Spatial Statistics for Large Datasets

We discuss the experiences and results of the AppStatUZH team's particip...