Trimmed minimax estimator of a covariance matrix
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Publication:1074983
DOI10.1007/BF02482503zbMath0591.62048MaRDI QIDQ1074983
Publication date: 1986
Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)
Monte Carlo simulationdifferential inequalityWishart distributionestimatorminimax estimatoradaptiveStein's lossmultivariate normal populationeigenvalues of the sample covariance matrixestimating the covariance matrixorthogonally invariant trimmed estimatorStein's truncation
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Cites Work
- Trimmed estimates in simultaneous estimation of parameters in exponential families
- An orthogonally invariant minimax estimator of the covariance matrix of a multivariate normal population
- Estimation of a covariance matrix under Stein's loss
- Simultaneous estimation of parameters under entropy loss
- An identity for the Wishart distribution with applications
- Estimation of the inverse covariance matrix: Random mixtures of the inverse Wishart matrix and the identity
- Empirical Bayes estimation of the multivariate normal covariance matrix
- Estimation of the mean of a multivariate normal distribution
- Multivariate empirical Bayes and estimation of covariance matrices
- Minimax estimators for a multinormal precision matrix
- On Truncation of Shrinkage Estimators in Simultaneous Estimation of Normal Means
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