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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.00176 |
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| _version_ | 1866911086631976960 |
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| author | Huang, Ping-Han Kao, Ming-Hung |
| author_facet | Huang, Ping-Han Kao, Ming-Hung |
| contents | Efficient data collection is essential in applied studies where frequent measurements are costly, time-consuming, or burdensome. This challenge is especially pronounced in functional data settings, where each subject is observed at only a few time points due to practical constraints. Most existing design approaches focus on selecting optimal time points for individual subjects, typically relying on model parameters estimated from a pilot study. However, the design of the pilot study itself has received limited attention. We propose a framework for constructing pilot-study designs that support both accurate trajectory recovery and effective planning of future designs. A search algorithm is developed to generate such high-quality pilot-study designs. Simulation studies and a real data application demonstrate that our approach outperforms commonly used alternatives, highlighting its value in resource-limited settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00176 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | New Pilot-Study Design in Functional Data Analysis Huang, Ping-Han Kao, Ming-Hung Methodology Applications Efficient data collection is essential in applied studies where frequent measurements are costly, time-consuming, or burdensome. This challenge is especially pronounced in functional data settings, where each subject is observed at only a few time points due to practical constraints. Most existing design approaches focus on selecting optimal time points for individual subjects, typically relying on model parameters estimated from a pilot study. However, the design of the pilot study itself has received limited attention. We propose a framework for constructing pilot-study designs that support both accurate trajectory recovery and effective planning of future designs. A search algorithm is developed to generate such high-quality pilot-study designs. Simulation studies and a real data application demonstrate that our approach outperforms commonly used alternatives, highlighting its value in resource-limited settings. |
| title | New Pilot-Study Design in Functional Data Analysis |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2508.00176 |