Electronic Journal of Statistics

Asymptotic properties of the maximum likelihood and cross validation estimators for transformed Gaussian processes

François Bachoc, José Betancourt, Reinhard Furrer, and Thierry Klein

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Abstract

The asymptotic analysis of covariance parameter estimation of Gaussian processes has been subject to intensive investigation. However, this asymptotic analysis is very scarce for non-Gaussian processes. In this paper, we study a class of non-Gaussian processes obtained by regular non-linear transformations of Gaussian processes. We provide the increasing-domain asymptotic properties of the (Gaussian) maximum likelihood and cross validation estimators of the covariance parameters of a non-Gaussian process of this class. We show that these estimators are consistent and asymptotically normal, although they are defined as if the process was Gaussian. They do not need to model or estimate the non-linear transformation. Our results can thus be interpreted as a robustness of (Gaussian) maximum likelihood and cross validation towards non-Gaussianity. Our proofs rely on two technical results that are of independent interest for the increasing-domain asymptotic literature of spatial processes. First, we show that, under mild assumptions, coefficients of inverses of large covariance matrices decay at an inverse polynomial rate as a function of the corresponding observation location distances. Second, we provide a general central limit theorem for quadratic forms obtained from transformed Gaussian processes. Finally, our asymptotic results are illustrated by numerical simulations.

Article information

Source
Electron. J. Statist., Volume 14, Number 1 (2020), 1962-2008.

Dates
Received: December 2019
First available in Project Euclid: 28 April 2020

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1588039327

Digital Object Identifier
doi:10.1214/20-EJS1712

Mathematical Reviews number (MathSciNet)
MR4091860

Zentralblatt MATH identifier
07200248

Subjects
Primary: 62M30: Spatial processes
Secondary: 62F12: Asymptotic properties of estimators

Keywords
Covariance parameters asymptotic normality consistency weak dependence random fields increasing-domain asymptotics

Rights
Creative Commons Attribution 4.0 International License.

Citation

Bachoc, François; Betancourt, José; Furrer, Reinhard; Klein, Thierry. Asymptotic properties of the maximum likelihood and cross validation estimators for transformed Gaussian processes. Electron. J. Statist. 14 (2020), no. 1, 1962--2008. doi:10.1214/20-EJS1712. https://projecteuclid.org/euclid.ejs/1588039327


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