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Activity Number: 508
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #321251 View Presentation
Title: Analysis of Asynchronous Longitudinal Data with Partially Linear Model
Author(s): Li Chen*
Companies: University of Missouri
Keywords: Asynchronous longitudinal data ; ernel weighted estimation ; Last observation carried forward ; Nonparametric regression

With the advancement of technology, multiple sources and facets of data, including both physical and virtual, are collected over time. These longitudinal data are often observed intermittently, at subject-specific times with mis-matched covariates and response. Existing methods for asynchronous longitudinal data analysis stipulates the linear relationship between longitudinal covariates and response. In this paper, we propose a new method for estimating the regression coefficients in a partially linear model. The asymptotic normality of the resulting estimators is established, with a robust sandwich standard deviation formula. Simulation studies support our theoretical findings and show its favorable performance with competing methods. Dataset from an HIV study is used to illustrate our methodology.

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

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