Optimal design of experiments for functional linear models with dynamic factors

TL;DR

This paper develops optimal experimental designs for accurately estimating the functional coefficient in a function-on-function linear regression model, considering dynamic factors and providing practical algorithms and examples.

Contribution

It introduces a novel framework for designing experiments that optimize the estimation of functional coefficients in dynamic functional linear models.

Findings

Derived variance-covariance matrix for the estimator

Provided explicit formulas for A- and D-optimal designs

Demonstrated methodology with simulated data

Abstract

In this work we build optimal experimental designs for precise estimation of the functional coefficient of a function-on-function linear regression model where both the response and the factors are continuous functions of time. After obtaining the variance-covariance matrix of the estimator of the functional coefficient which minimizes the integrated sum of square of errors, we extend the classical definition of optimal design to this estimator, and we provide the expression of the A-optimal and of the D-optimal designs. Examples of optimal designs for dynamic experimental factors are then computed through a suitable algorithm, and we discuss different scenarios in terms of the set of basis functions used for their representation. Finally, we present an example with simulated data to illustrate the feasibility of our methodology.