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Title: Pilot study designs for sparse functional data Authors:  Ping-Han Huang - Arizona State University (United States) [presenting]
MingHung Kao - Arizona State University (United States)
Abstract: In sparse functional data analysis (SFDA), the number of repeated measures per subject is often limited by practical constraints such as costs. In light of this issue, a number of approaches have been developed to find (locally) optimal designs to help increase the efficiency of SFDA. The success of these design methods greatly hinges on the accurate prior information from pilot studies. However, the selection of a good pilot study design remains unclear. The aim is to fill this gap by proposing new hybrid designs that combine some combinatorial designs with a 'notorious' type of FDA designs. Through simulation studies, we demonstrate that our proposed designs can outperform the widely used simple random designs to facilitate the use of previously developed locally optimal design approaches.