Spectral methods in linear minimax estimation
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Publication:1914881
DOI10.1007/BF00046985zbMath0844.62011MaRDI QIDQ1914881
Publication date: 1 September 1996
Published in: Acta Applicandae Mathematicae (Search for Journal in Brave)
optimalitymaximal eigenvalueeigenspaceBayesian approachcircular constraintsexperimental design approachexplicit estimation formulasHoffmann-Läuter estimatorleft spectral equationminimax-linear estimatorright spectral equation
Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Minimax procedures in statistical decision theory (62C20) Eigenvalues, singular values, and eigenvectors (15A18)
Related Items (6)
Linear minimax estimation with ellipsoidal constraints ⋮ On the equivalence of spectral theory and Bayesian analysis in minimax linear estimation ⋮ RAP-method (random perturbation method) for minimax \(G\)-filter ⋮ Near-optimality of linear recovery in Gaussian observation scheme under \(\| \cdot \|_{2}^{2}\)-loss ⋮ Simultaneous Canonization of Linear Models ⋮ RAP-method (random perturbation method) for finding \(S\)-minimax control vectors and parameter estimates for some linear systems with random coefficients
Cites Work
- On general minimax theorems
- Minimax linear regression estimation with symmetric parameter restrictions
- Linear transformations preserving best linear unbiased estimators in a general Gauss-Markoff model
- General equivalence theory for optimum designs (approximate theory)
- On the equivalence of spectral theory and Bayesian analysis in minimax linear estimation
- Characterization of minimax linear estimators in linear regression
- Approximate linear minimax estimation in regression analysis with ellipsoidal constraints
- Approximate minimax estimation in linear regression: theoretical results
- A minimax linear estimator for linear parameters under restrictions in form of inequalities
- Bayes, Admissible, and Minimax Linear Estimators in Linear Models with Restricted Parameter Space
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