An algorithm for calculating D-optimal designs for polynomial regression with prior information and its appilications

TL;DR

This paper introduces an algorithm that leverages canonical moments and discrete integrable systems to efficiently compute D-optimal designs for polynomial regression models with prior information, enhancing experimental design methods.

Contribution

It presents a novel algorithm connecting canonical moments with integrable systems to calculate D-optimal designs for models with prior information.

Findings

The algorithm effectively computes D-optimal designs for polynomial regression with prior info.

Applications demonstrate the algorithm's practical utility in statistical experiment design.

Abstract

Optimal designs are required to make efficient statistical experiments. D-optimal designs for some models are calculated by using canonical moments. On the other hand, integrable systems are dynamical systems whose solutions can be written down concretely. In this paper, polynomial regression models with prior information are discussed. In order to calculate D-optimal designs for these models, a useful relationship between canonical moments and discrete integrable systems is used. By using canonical moments and discrete integrable systems, an algorithm for calculating D-optimal designs for these models is proposed. Then some examples of applications of the algorithm are introduced.