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Activity Number: 503 - SPAAC Poster Competition
Type: Topic Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Scientific and Public Affairs Advisory Committee
Abstract #324103
Title: Dynamic Outlier Detection for Functional Data
Author(s): Meredith King* and Ana-Maria Staicu and Luo Xiao and Ciprian M Crainiceanu
Companies: North Carolina State University and North Carolina State University, Department of Statistics and North Carolina State University and Johns Hopkins University
Keywords: functional data ; outlier detection ; functional principal component analysis

Outlier detection methods for functional data have been evolving over the past decade, but currently these approaches only detect outliers from a set of complete curves. While this procedure is appropriate for identifying outliers to aid in further analysis of the data, there are many situations for which we would like to detect an outlier dynamically or in real-time so we can intervene. For example, in the case of child growth curves, it would be beneficial for a child to be identified as an outlier as soon as possible so that appropriate medical care can be administered. Inspired by this problem, we consider dynamic outlier detection of outlying observations for functional data observed discretely. Our proposed method is based on functional principal component analysis. We illustrate the method numerically through simulations and through a real data application.

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

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