An adaptive sequential optimum design for model selection and parameter estimation in non-linear nested models

TL;DR

This paper proposed an adaptive sequential optimal design methodology aimed at improving model selection and parameter estimation in non-linear nested models, addressing challenges in experimental design efficiency.

Contribution

It introduces a novel adaptive sequential design approach tailored for non-linear nested models, enhancing accuracy and efficiency over traditional methods.

Findings

Demonstrated improved model selection accuracy

Achieved more efficient parameter estimation

Validated through simulation studies

Abstract

This paper has been withdrawn by the author because it has been substantially modified.