New Pilot-Study Design in Functional Data Analysis

Abstract

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.