Adaptations in the simulated annealing algorithm to generate D-optimal mixing experiments both in the absence and presence of biases in the specification of the proportions-Reference-Cited by-同舟云学术

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Adaptations in the simulated annealing algorithm to generate D-optimal mixing experiments both in the absence and presence of biases in the specification of the proportions

Published:2024-05-09 Issue: Volume: Page:1-16
ISSN:2373-7484
Container-title:Communications in Statistics: Case Studies, Data Analysis and Applications
language:en
Short-container-title:Communications in Statistics: Case Studies, Data Analysis and Applications

Author:

Cirillo Marcelo Angelo¹^ORCID,de Menezes Fortunato Silva²^ORCID

Affiliation:

1. Institute of Exact Sciences and Technology (ICET), Department of Statistics (DES), Federal University of Lavras (UFLA), Lavras, Minas Gerais, Brazil

2. Institute of Natural Sciences (ICN), Physics Department (DFI), Federal University of Lavras (UFLA), Lavras, Minas Gerais, Brazil

Funder

Brazilian Agency CNPq

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23737484.2024.2350393

Reference32 articles.

1. Experimentation order in factorial designs with 8 or 16 runs

2. Alternative modeling techniques for the quantal response data in mixture experiments

3. Atkinson, A. C., A. N. Donev, and R. D. Tobias. 2007. Optimum Experimental Designs with SAS. Oxford: Oxford University Press.

4. Bartal, Y., A. Fiat, H. Karloff, and R. Vohra. 1992. “New Algorithms for an Ancient Scheduling Problem.” In Proceedings of the 24th ACM Symposium on the Theory of Computing, 51–58. https://doi.org/10.1145/129712.129718.

5. General Blending Models for Data From Mixture Experiments

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