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Activity Number: 595 - Dynamic Methods for Functional Data with Application to Clinical Data Analysis
Type: Invited
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #325434 View Presentation
Title: Dynamic Prediction Intervals for Functional Data
Author(s): Nicholas Rios* and Andrada E. Ivanescu
Companies: Montclair State University and Montclair State University, Department of Mathematical Sciences
Keywords: functional regression ; dynamic models ; function-on-function regression

The prediction of functional data samples has been the focus of several functional data analysis endeavors. We consider the use of dynamic function-on-function regression for dynamic prediction of the future trajectory as well as the construction of dynamic prediction intervals for functional data. The efficacy of Dynamic Penalized Function-on-Function Regression (DPFFR) was assessed and comparisons of DPFFR prediction intervals with other dynamic prediction methods were considered. To make these comparisons, metrics were used to measure prediction error, prediction interval width, and prediction interval coverage. Simulations and applications to biomedical data illustrate the usefulness of the dynamic functional prediction methods.

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