Numerical methods for linear minimax estimation
Copyright policy )Numerical methods for linear minimax estimation
Summary: 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.
parametric restrictions, Estimation in multivariate analysis, Minimax procedures in statistical decision theory, Computational problems in statistics, p-mean, quasi Newton method, linear model, Bayes estimation, duality, L-optimality, mean squared error, minimax estimation, non-smooth optimization
parametric restrictions, Estimation in multivariate analysis, Minimax procedures in statistical decision theory, Computational problems in statistics, p-mean, quasi Newton method, linear model, Bayes estimation, duality, L-optimality, mean squared error, minimax estimation, non-smooth optimization
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