ArticleChemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society2017
Standardized maximim
Article in Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Nature-inspired metaheuristics for optimizing dose-finding and computationally challenging clinical trial designs.Clinical trials (London, England) · 2025Article
- Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines.Scientific reports · 2024Article
- Optimal Designs for Nonlinear Mixed-effects Models Using Competitive Swarm Optimizer with Mutated Agents.Research square · 2023Article
- Impact of the Error Structure on the Design and Analysis of Enzyme Kinetic Models.Statistics in biosciences · 2023Article
- Metaheuristics for pharmacometrics.CPT: pharmacometrics & systems pharmacology · 2021Review
- Article
- Using Differential Evolution to Design Optimal Experiments.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2020Article
- Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design.Frontiers in bioengineering and biotechnology · 2019Article
- A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose.Statistical papers (Berlin, Germany) · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters. An alternative is the maximin approach that allows the user to specify a range of values for each parameter of interest. However, the maximin approach is difficult because we first have to determine the locally optimal design for each set of nominal values before maximin types of optimal designs can be found via a nested optimization process. We show that particle swarm optimization (PSO) techniques can solve such complex optimization problems effectively. We demonstrate numerical results from PSO can help find, for the first time, formulae for standardized maximin
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.