ArticleBiometrical journal. Biometrische Zeitschrift2020
A design criterion for symmetric model discrimination based on flexible nominal sets.
Article in Biometrical journal. Biometrische Zeitschrift, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Impact of the Error Structure on the Design and Analysis of Enzyme Kinetic Models.Statistics in biosciences · 2023Article
- Optimal Bayesian design for model discrimination via classification.Statistics and computing · 2022Article
- A design criterion for symmetric model discrimination based on flexible nominal sets.Biometrical journal. Biometrische Zeitschrift · 2020Article
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2 authors.
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Abstract
Experimental design applications for discriminating between models have been hampered by the assumption to know beforehand which model is the true one, which is counter to the very aim of the experiment. Previous approaches to alleviate this requirement were either symmetrizations of asymmetric techniques, or Bayesian, minimax, and sequential approaches. Here we present a genuinely symmetric criterion based on a linearized distance between mean-value surfaces and the newly introduced tool of flexible nominal sets. We demonstrate the computational efficiency of the approach using the proposed criterion and provide a Monte-Carlo evaluation of its discrimination performance on the basis of the likelihood ratio. An application for a pair of competing models in enzyme kinetics is given.
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Registered trials
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