New Pilot-Study Design in Functional Data Analysis

Ping-Han Huang, Ming-Hung Kao
Arizona State University

A primary focus on design problems in sparse functional data has revolved around finding the best time points to collect observations from subjects. Previous work has yielded locally optimal designs that rely on the estimation of unknown parameters from pilot studies. In contrast to the existing work, our study focuses on formulating a good pilot-study design to facilitate identifying optimal designs for subjects in the next study as well as recovering trajectories for subjects in the pilot study. A search algorithm is developed to generate such highquality pilot-study designs. We further demonstrate the usefulness of our designs by comparing with balanced incomplete block designs and random designs. Our simulation studies and real data application show that our designs yield better performance than the competing designs.