Hybrid exact-approximate design approach for sparse functional data

Optimal designs for sparse functional data under the functional empirical component (FEC) settings are studied. This design issue has some unique features, making it different from classical design problems. To efficiently obtain optimal exact and approximate designs, new computational methods and useful theoretical results are developed, and a hybrid exact -approximate design approach is proposed. The proposed methods are demonstrated to be efficient via simulation studies and a real example.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Optimal designs for sparse functional data under the functional empirical component (FEC) settings are investigated. New computational methods and theoretical results are developed to efficiently obtain optimal exact and approximate designs. A hybrid exact-approximate design approach is proposed and demonstrated to be efficient through simulation studies and a real example.