Showing papers by "Luc Pronzato published in 1985"
TL;DR: In this paper, a new methodology is proposed for robust experiment design, which allows uncertainly in the nominal parameters of the model under study to be taken into account by assuming that these parameters belong to some population with known statistics.
Abstract: A new methodology is proposed for robust experiment design. It allows uncertainly in the nominal parameters of the model under study to be taken into account by assuming that these parameters belong to some population with known statistics. The mathematical expectation of the determinant of the Fisher information matrix over this population is here taken as a measure of optimality, but the expectation of other nonrobust criteria could have been considered as well. Stochastic approximation techniques are advocated as the simplest tools for optimizing these robust criteria. The efficiency of the proposed algorithms is demonstrated on simple examples—for which an analytical solution exists—as well as on more complex ones. A comparison is made with Landaw's suboptimal approach, which supports an interesting conjecture about the robustness of replicate samples.
175 citations
TL;DR: In this paper, the determinant of the Fisher information matrix and its inverse were considered, and robust criteria were obtained for their optimization, and stochastic approximation methods were used to evaluate the robustness of these functions.
9 citations
TL;DR: The maximization of this non-differentiable criterion is carried out by a global optimization routine using an adaptive random search strategy which is demonstrated to furnish very satisfactory results.
2 citations