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Activity Number:
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89
- Nonparametric Methods for Modern Data
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Type:
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Contributed
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Date/Time:
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Monday, August 9, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #318123
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Title:
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Functional Data Analysis for Longitudinal Data with Informative Observations
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Author(s):
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Caleb Weaver* and Luo Xiao and Wenbin Lu
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Companies:
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Department of Statistics, North Carolina State University and Department of Statistics, North Carolina State University and North Carolina State University
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Keywords:
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Functional data analysis;
Longitudinal data;
Informative observations;
Penalized splines
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Abstract:
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Models of longitudinal data typically assume that the observation process is fixed, or at least non-informative. However, this assumption is often violated in real data, which results in bias within models that fail to account for the dependence between observation times and longitudinal outcomes. In this article, we address the application of functional data analysis to longitudinal data with informative observations. Methods for appropriately modeling this data using common techniques of functional data analysis are discussed. Theoretical analysis and numerical simulations demonstrate the effectiveness of the proposed methods. These methods are applied to a longitudinal study of patient-reported symptom severity in Parkinson's disease.
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Authors who are presenting talks have a * after their name.
