A Metaheuristic Adaptive Cubature Based Algorithm to Find Bayesian Optimal Designs for Nonlinear Models

DOI10.1080/10618600.2019.1601097OpenAlexW2927015176WikidataQ128120680 ScholiaQ128120680MaRDI QIDQ134019

Heinz Holling, Belmiro P. M. Duarte, Weng Kee Wong, Ehsan Masoudi, Weng Kee Wong, Heinz Holling, Ehsan Masoudi, Belmiro P. M. Duarte

Publication date: 5 June 2019

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://escholarship.org/uc/item/8zj5m7g0

zbMATH Keywords

equivalence theoremFisher information matrix\(D\)-efficiencyP-optimalitycompound criterionadaptive subregion algorithm

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (6)

Optimal Design of Experiments for Implicit Models ⋮ Using CVX to Construct Optimal Designs for Biomedical Studies with Multiple Objectives ⋮ Optimal experimental design for linear time invariant state-space models ⋮ Optimal design of multifactor experiments via grid exploration ⋮ ICAOD ⋮ \(G\)-optimal designs for hierarchical linear models: an equivalence theorem and a nature-inspired meta-heuristic algorithm

Uses Software

• DCUHRE
• R
• GAMS
• LLTM
• Cubature
• ICAOD

Cites Work

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