Estimating the mean function of a Gaussian process and the Stein effect

DOI10.1016/0047-259X(83)90018-0zbMath0521.62071WikidataQ56941523 ScholiaQ56941523MaRDI QIDQ1055133

Robert L. Wolpert, James O. Berger

Publication date: 1983

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

minimaxBrownian motionBrownian bridgeKarhunen-Loeve expansionrisk functionStein effectweighted quadratic lossglobal estimation of mean functions of continuous Gaussian processesincorporation of prior information

Mathematics Subject Classification ID

Non-Markovian processes: estimation (62M09) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Admissibility in statistical decision theory (62C15)

Related Items (6)

Estimation of the drift of a Gaussian process under balanced loss function ⋮ On Minimaxity and Limit of Risks Ratio of James-Stein Estimator Under the Balanced Loss Function ⋮ Stein estimation for the drift of Gaussian processes using the Malliavin calculus ⋮ Estimators for the Drift of Subfractional Brownian Motion ⋮ Maximum likelihood estimation for Gaussian process with nonlinear drift ⋮ Shrinkage estimation in the two-way multivariate normal model

Cites Work

• Statistical decision theory. Foundations, concepts, and methods
• A robust generalized Bayes estimator and confidence region for a multivariate normal mean
• Selecting a minimax estimator of a multivariate normal mean
• Minimax estimation of a multivariate normal mean under arbitrary quadratic loss
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Estimating the mean function of a Gaussian process and the Stein effect