Spatial adaption for predicting random functions
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Publication:1307098
DOI10.1214/aos/1024691470zbMath0927.62098OpenAlexW2050655021MaRDI QIDQ1307098
Thomas Müller-Gronbach, Klaus Ritter
Publication date: 14 December 1999
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aos/1024691470
Inference from stochastic processes and prediction (62M20) Gaussian processes (60G15) Sequential statistical design (62L05) Prediction theory (aspects of stochastic processes) (60G25)
Related Items (7)
The optimal uniform approximation of systems of stochastic differential equations ⋮ Optimal designs for weighted approximation and integration of stochastic processes on \([0,\infty)\) ⋮ Estimation of number of the derivatives of a Gaussian process ⋮ Global smoothness estimation of a Gaussian process from general sequence designs ⋮ Bayesian optimal sequential design for nonparametric regression via inhomogeneous evolutionary MCMC ⋮ Assessing the number of mean square derivatives of a Gaussian process ⋮ Uniform reconstruction of Gaussian processes
Cites Work
- Bayesian object identification: variants
- Sampling designs for estimating integrals of stochastic processes
- Deterministic and stochastic error bounds in numerical analysis
- Sojourns and extremes of Gaussian processes
- Asymptotic optimality of regular sequence designs
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Sampling designs for estimation of a random process
- Bounds on the efficiency of linear predictions using an incorrect covariance function
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- Locally lattice sampling designs for isotropic random fields
- Noisy Information and Computational Complexity
- Ideal spatial adaptation by wavelet shrinkage
- Estimating Functionals of a Stochastic Process
- Cuspidal Modular Symbols are Transportable
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- Bounds on Moments of Certain Random Variables
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