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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 #322118
Title: Identification of Outliers in Dynamic Functional Regression for Child Growth Studies
Author(s): Andrada E. Ivanescu* and Ciprian M Crainiceanu and William Checkley
Companies: Montclair State University, Department of Mathematical Sciences and Johns Hopkins University and Johns Hopkins University
Keywords: child height ; functional data ; functional regression ; dynamic outliers

We introduce a class of dynamic regression models designed to predict the future of growth curves based on their historical dynamics. This class of models incorporates both baseline and time-dependent covariates, start with simple regression models and build up to dynamic function-on-function regressions. Models from dynamic regression will be used to provide predictions of future observations and trajectories. Deviations from these predictions will be used for early detection of outlying observations or growth patterns. Methods are motivated by and applied to the CONTENT data set, a study that collected monthly child growth data on 197 children from birth until month 15.

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

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