Algorithms for Optimal Design with Application to Multiple Polynomial Regression.
Optimal and Robust Invariant Designs for Cubic Multiple Regression.
Numerical methods for linear minimax estimation
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.
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