Optimal Design of Experiments for Implicit Models-Reference-Cited by-同舟云学术

Optimal Design of Experiments for Implicit Models

Published:2021-02-03 Issue: Volume: Page:1-14
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Duarte Belmiro P. M.¹²,Atkinson Anthony C.³,Granjo José F. O.²,Oliveira Nuno M. C.²

Affiliation:

1. Department of Chemical and Biological Engineering, Instituto Politécnico de Coimbra, Instituto Superior de Engenharia de Coimbra, Coimbra, Portugal;;

2. CIEPQPF, Department of Chemical Engineering, University of Coimbra, Coimbra, Portugal;;

3. Department of Statistics, London School of Economics, London, UK

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2020.1862670

Reference90 articles.

1. Optimal Designs for Implicit Models

2. Compound and other optimum designs for systems of nonlinear differential equations arising in chemical kinetics

3. AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology

4. Numerical methods for optimum experimental design in DAE systems

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Sequential optimal experimental design for vapor-liquid equilibrium modeling;Chemical Engineering Science;2024-12

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

3. Optimal designs for dose-escalation trials and individual allocations in cohorts;Statistics and Computing;2022-10

4. Optimal design of mixture experiments for general blending models;Chemometrics and Intelligent Laboratory Systems;2021-10

5. Model-Based Design of Experiments for High-Dimensional Inputs Supported by Machine-Learning Methods;Processes;2021-03-11