An optimal experimental design criterion for discriminating between non‐normal models

By J. López–Fidalgo, Chiara Tommasi, Paula Camelia Trandafir. 2007

119 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d3794d13255309f91b5fd0e5178986dae1d2d933

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Douglas P. Wiens
2. Luzia A. Trinca
3. Holger Dette
4. Steven G. Gilmour

In Management Science and Operations Research

1. Douglas P. Wiens
2. Luzia A. Trinca
3. Holger Dette
4. Steven G. Gilmour
5. Masashi Sugiyama

In Statistics and Probability

1. Luzia A. Trinca
2. Holger Dette
3. Steven G. Gilmour
4. Douglas P. Wiens
5. I. Van Keilegom
6. Natalie Neumeyer
7. Johan Segers
8. Masashi Sugiyama

Citations in the corpus

• Optimal discrimination designs (2009, background): “the monographs of Silvey (1980), Pázman (1986), Atkinson and Donev (1992) or Pukelsheim (1993)] much less attention has been paid to the problem of designing experiments for model discrimination.”
• Optimum design of experiments for statistical inference (2012, methodology): “For this approach see for example Atkinson and Fedorov (1975) and López-Fidalgo et al. (2007) where they use T -optimality and Kullback–Leibler distance optimality as an extension of the first to non-normal models.”
• Robust discrimination designs (2009, background): “López-Fidalgo et al. (2007) studied extensions of these notions to non-normal models, leading to the maximization of inf θ0 [∫ I{η0.x|θ0/, η1.x|θ1/}ξ.dx/ ] ; this criterion is termed KL-optimality.”