Scholarlink

<p>In this work, we build optimal experimental designs for precise estimation of the functional coefficient of linear regression model where both the response and the factors are continuous functions. 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 illustrative example inspired by a real application in a pharmaceutical manufacturing process, to illustrate the feasibility and the advantages of our methodology.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimal design of experiments for functional linear models with dynamic factors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月12日

查看全文
  • Caterina May,
  • Theodoros Ladas,
  • Davide Pigoli,
  • Kalliopi Mylona

摘要

In this work, we build optimal experimental designs for precise estimation of the functional coefficient of linear regression model where both the response and the factors are continuous functions. 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 illustrative example inspired by a real application in a pharmaceutical manufacturing process, to illustrate the feasibility and the advantages of our methodology.