Non parametric estimation of smooth stationary covariance functions by interpolation methods

DOI10.1007/s11203-007-9014-zzbMath1204.62148OpenAlexW2051124955MaRDI QIDQ623485

S. N. Elogne, Christine Thomas-Agnan, Olivier Perrin

Publication date: 5 February 2011

Published in: Statistical Inference for Stochastic Processes (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11203-007-9014-z

zbMATH Keywords

spline interpolationgeostatisticsstationary processescovariance estimationpositive definite

Mathematics Subject Classification ID

Related Items (4)

Estimation of Systematic and Spatially Correlated Components of Random Signals from Repeated Measurements: Application to Contrast Enhanced Computer Tomography Measurements ⋮ Adaptive covariance estimation with model selection ⋮ Nonparametric estimation of covariance functions by model selection ⋮ Nonparametric autocovariance estimation from censored time series by Gaussian imputation

Cites Work

• Positive definite functions and generalizations, an historical survey
• Properties of nonparametric estimators of autocovariance for stationary random fields
• Variogram fitting with a general class of conditionally nonnegative definite functions
• On the nonparametric estimation of covariance functions
• Spline approximation of random processes and design problems
• Mathematical Considerations in the Estimation of Spectra
• Polynomial Interpolation of Randomly Sampled Bandlimited Functions and Processes
• Simulation and Approximation of Stochastic Processes by Spline Functions
• Non-parametric covariance estimation from irregularly-spaced data
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