On prediction error variance to determining optimal design for two variable quadratic logistic model. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Adebola, F. B.; Fasoranbaku, O. A.; Kupolusi, J. A.
• Editors: Ling, Nengxiang
• Is Part Of: Cogent mathematics & statistics. Volume 7:Issue 1(2020)
• Publication Date: 2020-01-01
• Subjects: prediction error variance (PEV) -- general equivalence theorem -- generalized linear model -- optimal design and quadratic logistic models; Mathematics -- Periodicals; Statistics -- Periodicals; Mathematics; Statistics; Periodicals; 510
• DOI: 10.1080/25742558.2020.1853888
• Languages: English
• ISSNs: 2574-2558

Abstract

Abstract: Optimal design of experiment for logistic models has been examined and applied in a wide range of applications. The optimality of the designs is mostly determined by using general equivalence theorem with no attention paid to the extent at which the design can be useful for determining the predictive capability of the model. This paper addressed the predictive capability of optimal design model for two variable quadratic logistic regression model through prediction error variance(PEV). The PEV is a useful way to determining the predictive capability of a model in optimal design. The study used some initial guess parameters to represent any position of parameter in the design space through a simulation study of 10000 experimental runs. The design was optimal when the PEV value is less than one at nine equally weighted support points. The result of the analysis was able to identify the design that is good for prediction among all the designs obtained and conclude that prediction error variance should be used to test the stability of optimal design of experiment for two variable quadratic logistic models.