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Activity Number: 194
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #320367
Title: Dynamic Modeling with Conditional Quantile Trajectories for Longitudinal Snippet Data, with Application to Cognitive Decline of Alzheimer's Patients
Author(s): Matthew Dawson* and Hans-Georg Mueller
Companies: University of California at Davis and University of California at Davis
Keywords: functional data analysis ; autonomous differential equation ; monotonic processes

Many longitudinal data are plagued with sparsity of time points where measurements are available, and the functional data analysis perspective provides an effective and flexible approach to address this problem. We focus on the scenario where available data can be characterized as snippets, which are short stretches of longitudinal measurements. For each subject the stretch of available data are much shorter than the time frame of interest. An added challenge is introduced if a time proxy that is basic for usual longitudinal modeling is not available. This situation arises in the case of Alzheimer's disease and other data where one is interested in time dynamics of declining performance, but the time of disease onset is unknown. Our main methodological contribution is a novel approach to obtain uniformly consistent estimates of conditional quantile trajectories for monotonic processes as solutions of a dynamic system. We illustrate our methods with simulations and snippet data from an Alzheimer's study.

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

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