Abstract
Most research in design of experiments focuses on appropriate designs for a system with just one type of response, rather than multiple responses. In a decision-making process, relying on only one objective can lead to oversimplified, suboptimal choices that ignore important considerations. Consequently, the problem of constructing a design for an experiment when multiple types of responses are of interest often does not have a single definitive answer, particularly when the response variables have different distributions. Each of these response distributions imposes different requirements on the experimental design. Computer-generated optimal designs are popular design choices for less standard scenarios where classical designs are not ideal. This work presents a new approach to experimental designs for dual-response systems. The normal and binomial distributions are considered as potential responses. Using the D-criterion for the linear model and the Bayesian D-criterion for the logistic regression model, a weighted criterion is implemented in a coordinate-exchange algorithm. Designs are evaluated and compared across different weights. The sensitivity of the designs to the priors supplied for the Bayesian D-criterion is also explored.
Original language | English (US) |
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Pages (from-to) | 3034-3054 |
Number of pages | 21 |
Journal | Quality and Reliability Engineering International |
Volume | 37 |
Issue number | 7 |
DOIs | |
State | Published - Nov 2021 |
Keywords
- Bayesian D-optimal design
- case studies
- desirability function
- dual-response nonlinear model
- experimental design
- optimal design
- reliability
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Management Science and Operations Research