Optimal experimental designs for ordinal models with mixed factors for industrial and healthcare applications-Reference-Cited by-同舟云学术

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Optimal experimental designs for ordinal models with mixed factors for industrial and healthcare applications

Published:2020-11-02 Issue: Volume: Page:1-13
ISSN:0022-4065
Container-title:Journal of Quality Technology
language:en
Short-container-title:Journal of Quality Technology

Author:

Lukemire Joshua¹,Mandal Abhyuday²,Wong Weng Kee³

Affiliation:

1. Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia

2. Department of Statistics, University of Georgia, Athens, Georgia

3. Department of Biostatistics, University of California at Los Angeles, Los Angeles, California

Funder

the National Institute of General Medical Sciences of the National Institutes of Health

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management,Safety, Risk, Reliability and Quality

Link

https://www.tandfonline.com/doi/pdf/10.1080/00224065.2020.1829215

Reference35 articles.

1. Standardized maximim D -optimal designs for enzyme kinetic inhibition models

2. Minimax optimal designs via particle swarm optimization methods

3. Locally Optimal Designs for Estimating Parameters

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1. A Double Exponential Particle Swarm Optimization with non-uniform variates as stochastic tuning and guaranteed convergence to a global optimum with sample applications to finding optimal exact designs in biostatistics;Applied Soft Computing;2024-09

2. ForLion: a new algorithm for D-optimal designs under general parametric statistical models with mixed factors;Statistics and Computing;2024-07-18

3. Best (but oft forgotten) practices: Efficient sample sizes for commonly used trial designs;The American Journal of Clinical Nutrition;2023-06

4. Optimal experimental designs for clustered read-out data of reliability tests via particle swarm optimization;Computers & Industrial Engineering;2022-09

5. Optimising process and product performance in complex systems: a study in the automotive industry;International Journal of Quality & Reliability Management;2022-07-15

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