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.

中文翻译：

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稀疏函数数据的混合精确近似设计方法

研究了函数经验分量（FEC）设置下稀疏函数数据的优化设计。该设计问题有一些独特之处，使其不同于经典设计问题。为了有效地获得最佳精确和近似设计，开发了新的计算方法和有用的理论结果，并提出了混合精确近似设计方法。通过模拟研究和实际例子证明所提出的方法是有效的。