Hybrid exact-approximate design approach for sparse functional data

Ming-Hung Kao and Ping-Han Huang

Computational Statistics & Data Analysis, 2024, vol. 190, issue C

Abstract: 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.

Keywords: Design efficiency; Longitudinal data; Mixed model equations; Principal components; Random effects (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:190:y:2024:i:c:s0167947323001615

DOI: 10.1016/j.csda.2023.107850

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