Which Parameterization of the Mat\'ern Covariance Function?

TL;DR

This paper compares different parameterizations of the Matérn covariance function, analyzing their effectiveness, estimation accuracy, and computational efficiency in spatial modeling, and provides recommendations for practitioners.

Contribution

It systematically evaluates the properties and performances of the three most popular Matérn parameterizations using large-scale data and advanced software tools.

Findings

Different parameterizations have distinct estimation and modeling properties.

The study demonstrates the feasibility of inferring all parameters simultaneously.

Recommendations are provided for selecting the appropriate parameterization based on data and modeling needs.

Abstract

The Mat\'ern family of covariance functions is currently the most popularly used model in spatial statistics, geostatistics, and machine learning to specify the correlation between two geographical locations based on spatial distance. Compared to existing covariance functions, the Mat\'ern family has more flexibility in data fitting because it allows the control of the field smoothness through a dedicated parameter. Moreover, it generalizes other popular covariance functions. However, fitting the smoothness parameter is computationally challenging since it complicates the optimization process. As a result, some practitioners set the smoothness parameter at an arbitrary value to reduce the optimization convergence time. In the literature, studies have used various parameterizations of the Mat\'ern covariance function, assuming they are equivalent. This work aims at studying the effectiveness of different parameterizations under various settings. We demonstrate the feasibility of inferring all parameters simultaneously and quantifying their uncertainties on large-scale data using the ExaGeoStat parallel software. We also highlight the importance of the smoothness parameter by analyzing the Fisher information of the statistical parameters. We show that the various parameterizations have different properties and differ from several perspectives. In particular, we study the three most popular parameterizations in terms of parameter estimation accuracy, modeling accuracy and efficiency, prediction efficiency, uncertainty quantification, and asymptotic properties. We further demonstrate their differing performances under nugget effects and approximated covariance. Lastly, we give recommendations for parameterization selection based on our experimental results.