. In linear models the breakdown point of an estimator depends strongly on the underlying design. This holds in particular for high breakdown point estimators as the least median of squares estimator or least trimmed squares estimators. It could be shown that the breakdown point is maximized if the number of regressors which lie in a subspace is minimized. This means in particular that the number of repetitions of experimental conditions should be minimized. Usually this leads to designs which are very different from the classically optimal designs. But in some situations breakdown point maximizing designs can be found which are also optimal in the classical sense. In this paper two examples are given where breakdown point maximizing design...
On D-optimality of exact linear regression designs with minimum support. - In: Journal of statistica...
125 A generalization of the classical A-optimality criterion for designs is derived by defining opti...
In a recent paper Snee and Marquardt (1974) considered designs for linear mixture models, where the ...
In robust statistics, the concept of breakdown point was introduced to quantify the robustness of an...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
A brief survey is given of characterizations of optimal experimental designs in the approximate desi...
Abstract. Within the framework of classical linear regression model optimal design criteria of stoch...
A brief survey is given of characterizations of optimal experimental designs in the approximate desi...
A brief survey is given of characterizations of optimal experimental designs in the approximate desi...
Key Words: design optimality, fraction of design space technique, non-regular desig
We consider maximin and Bayesian D-optimal designs for nonlinear regression models. The maximin crit...
On D-optimality of exact linear regression designs with minimum support. - In: Journal of statistica...
On D-optimality of exact linear regression designs with minimum support. - In: Journal of statistica...
125 A generalization of the classical A-optimality criterion for designs is derived by defining opti...
In a recent paper Snee and Marquardt (1974) considered designs for linear mixture models, where the ...
In robust statistics, the concept of breakdown point was introduced to quantify the robustness of an...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
A brief survey is given of characterizations of optimal experimental designs in the approximate desi...
Abstract. Within the framework of classical linear regression model optimal design criteria of stoch...
A brief survey is given of characterizations of optimal experimental designs in the approximate desi...
A brief survey is given of characterizations of optimal experimental designs in the approximate desi...
Key Words: design optimality, fraction of design space technique, non-regular desig
We consider maximin and Bayesian D-optimal designs for nonlinear regression models. The maximin crit...
On D-optimality of exact linear regression designs with minimum support. - In: Journal of statistica...
On D-optimality of exact linear regression designs with minimum support. - In: Journal of statistica...
125 A generalization of the classical A-optimality criterion for designs is derived by defining opti...
In a recent paper Snee and Marquardt (1974) considered designs for linear mixture models, where the ...