Lectures on the theory of estimation of many parameters
From MaRDI portal
Publication:1076455
DOI10.1007/BF01085007zbMath0593.62049MaRDI QIDQ1076455
Publication date: 1986
Published in: Journal of Soviet Mathematics (Search for Journal in Brave)
quadratic lossestimatingBayes approachnormal covariance matrixentropy of multinomial distributionestimation of mean of multidimensional normal distributionJames-Stein estimate
Estimation in multivariate analysis (62H12) Bayesian inference (62F15) Statistical aspects of information-theoretic topics (62B10)
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Cites Work
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- Tail minimaxity in location vector problems and its applications
- Generalized Bayes Solutions in Estimation Problems
- Empirical Bayes on vector observations: An extension of Stein's method
- Admissible Estimators, Recurrent Diffusions, and Insoluble Boundary Value Problems
- On Minimax Statistical Decision Procedures and their Admissibility
- On the Translation Parameter Problem for Discrete Variables
