Estimating the smoothness of a Gaussian random field from irregularly spaced data via higher-order quadratic variations

DOI10.1214/15-AOS1365zbMath1327.62482arXiv1510.08699MaRDI QIDQ892257

Wei-Liem Loh

Publication date: 18 November 2015

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1510.08699

zbMATH Keywords

Gaussian random fieldirregularly spaced dataMatérn covariancehigher-order quadratic variationsmoothness estimation

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40)

Related Items (15)

Asymptotically equivalent prediction in multivariate geostatistics ⋮ Local scaling limits of Lévy driven fractional random fields ⋮ Estimating the smoothness of a Gaussian random field from irregularly spaced data via higher-order quadratic variations ⋮ Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation ⋮ Fixed-domain asymptotic properties of maximum composite likelihood estimators for Gaussian processes ⋮ Estimating the parameters of some common Gaussian random fields with nugget under fixed-domain asymptotics ⋮ Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding ⋮ Hurst function estimation ⋮ Joint asymptotics for estimating the fractal indices of bivariate Gaussian processes ⋮ Hypothesis testing for the smoothness parameter of Matérn covariance model on a regular grid ⋮ On fixed-domain asymptotics, parameter estimation and isotropic Gaussian random fields with Matérn covariance functions ⋮ Smoothness estimation of nonstationary Gaussian random fields from irregularly spaced data observed along a curve ⋮ On information about covariance parameters in Gaussian Matérn random fields ⋮ Asymptotic Analysis of Maximum Likelihood Estimation of Covariance Parameters for Gaussian Processes: An Introduction with Proofs ⋮ Bayesian fixed-domain asymptotics for covariance parameters in a Gaussian process model

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