Numerical Methods for Linear Minimax Estimation
Preprint series: 98-19, Preprints
- 62F10 Point estimation
- 62J05 Linear regression
Abstract: We discuss two numerical approaches to linear minimax estimation in linear models under ellipsoidal parameter restrictions. The first attacks the problem directly, by minimizing the maximum risk among the estimators. The second method is based on the duality between minimax and Bayes estimation, and aims at finding a least favorable prior distribution.
Keywords: Bayes estimation, duality, linear model, L-optimality, mean squared error, minimax estimation, non-smooth optimization, parameter restrictions, p-mean, quasi Newton method
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