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Activity Number: 425 - Nonparametric Methods for Dependent Data
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #322469
Title: Modeling Longitudinal Skewed Functional Data
Author(s): Mohammad Samsul Alam* and Ana-Maria Staicu
Companies: North Carolina State University and North Carolina State University
Keywords: longitudinal; functional data; local-likelihood; copula; low-rank approximation; multiple sclerosis

This paper introduces a model for longitudinal functional data analysis by accounting for the presence of skewness. The proposed procedure decouples the marginal pointwise variation from the complex longitudinal and functional dependence using copula methodology. The pointwise variation is described through parametric distribution functions that capture varying skewness and vary smoothly both in time and over the functional argument. The joint dependence is quantified through a Gaussian copula with a low-rank approximation-based covariance. The introduced class of models provides an unifying platform for both pointwise quantile estimation and prediction of complete trajectories at new times. We investigate the methods numerically in simulations and discuss their application to a diffusion tensor imaging study of multiple sclerosis patients.

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

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