1. Optimal designs for dual response systems for the normal and binomial case.
- Author
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Burke, Sarah E., Montgomery, Douglas C., Anderson‐Cook, Christine M., Silvestrini, Rachel T., and Borror, Connie M.
- Subjects
GAUSSIAN distribution ,EXPERIMENTAL design ,LOGISTIC regression analysis ,ALGORITHMS ,BINOMIAL distribution ,REGRESSION analysis - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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