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Activity Number: 260
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #319197 View Presentation
Title: Optimal Design for Sampling Functional Data
Author(s): So-Young Park* and Luo Xiao and Jayson Wilbur and Ana-Maria Staicu
Companies: North Carolina State University and North Carolina State University and Metrum Research Group and North Carolina State University
Keywords: Sparse functional data ; Optimal design ; Functional linear model ; Functional principal component analysis ; Prediction ; Fetal Growth

We study the optimal design problem for sampling functional data. The goal is to find optimal time points for sampling functional data so that the full underlying true function can be accurately predicted. A similar problem occurs in functional regression, where the goal is to find optimal time points for sampling functional predictor in order to accurately predict the outcome of interest. The problems are motivated by the fetal growth study, where the objective is to determine the optimal times to collect ultrasound measurements and the number of ultrasound measurements that are needed to recover fetal growth trajectories or to predict child birth outcomes. Under the frameworks of functional principal component analysis and functional linear models, we formulate both problems as a unified optimization problem and the solution provides the optimal design points. We also propose a simple method for selecting the number of optimal sampling points. Performance of the proposed method is thoroughly investigated via a simulation study and by its application to fetal ultrasound.

Authors who are presenting talks have a * after their name.

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