Asymptotically efficient prediction of a random field with a missspecified covariance function

From MaRDI portal
Publication:1098531

DOI10.1214/aos/1176350690zbMath0637.62088OpenAlexW2095541970MaRDI QIDQ1098531

Michael L. Stein

Publication date: 1988

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

Full work available at URL: https://doi.org/10.1214/aos/1176350690



Related Items

Necessary and sufficient conditions for asymptotically optimal linear prediction of random fields on compact metric spaces, Equivalence of Gaussian measures for some nonstationary random fields, Asymptotic properties of multivariate tapering for estimation and prediction, Longitudinal analysis of spatially correlated data, Generating heterogeneity spectra from spatially resolved measurements, On the consistent separation of scale and variance for Gaussian random fields, Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes, A multi-resolution model for non-Gaussian random fields on a sphere with application to ionospheric electrostatic potentials, A Fused Gaussian Process Model for Very Large Spatial Data, Equivalence and orthogonality of Gaussian measures on spheres, Cross-validation estimation of covariance parameters under fixed-domain asymptotics, Space-Time Estimation and Prediction under Infill Asymptotics with Compactly Supported Covariance Functions, Asymptotic properties of the maximum likelihood and cross validation estimators for transformed Gaussian processes, Asymptotically equivalent prediction in multivariate geostatistics, Fixed-domain asymptotic properties of maximum composite likelihood estimators for Gaussian processes, Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions, On Prediction Properties of Kriging: Uniform Error Bounds and Robustness, Convergence of Gaussian Process Regression with Estimated Hyper-Parameters and Applications in Bayesian Inverse Problems, Estimation and prediction using generalized Wendland covariance functions under fixed domain asymptotics, A kernel approximation to the kriging predictor of a spatial process, Unifying compactly supported and Matérn covariance functions in spatial statistics, Some asymptotic properties of kriging when the covariance function is misspecified, On maximum likelihood estimation of parameters in incorrectly specified models of covariance for spatial data, On the stability of the geostatistical method, Spatial designs and properties of spatial correlation: effects on covariance estimation, Composite likelihood estimation for a Gaussian process under fixed domain asymptotics, Spatial dependence estimation using FFT of biased covariances, Sparse sampling: spatial design for monitoring stream networks, Maximum likelihood estimation of cloud height from multi-angle satellite imagery, A review of nonparametric hypothesis tests of isotropy properties in spatial data, Cross validation and maximum likelihood estimations of hyper-parameters of Gaussian processes with model misspecification, Isotropic Spectral Additive Models of the Covariogram, Maximum likelihood estimation for Gaussian processes under inequality constraints, Estimation and prediction of Gaussian processes using generalized Cauchy covariance model under fixed domain asymptotics, Fixed-domain asymptotic properties of tapered maximum likelihood estimators, Estimating functions evaluated by simulation: a Bayesian/analytic approach, Constructing and fitting models for cokriging and multivariable spatial prediction, On information about covariance parameters in Gaussian Matérn random fields, Interpolation of spatial data – A stochastic or a deterministic problem?, Asymptotic properties of a maximum likelihood estimator with data from a Gaussian process, A comparison of sampling grids, cut-off distance and type of residuals in parametric variogram estimation, 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