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Models for Intensive Longitudinal Data$
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Theodore A. Walls and Joseph L. Schafer

Print publication date: 2006

Print ISBN-13: 9780195173444

Published to Oxford Scholarship Online: March 2012

DOI: 10.1093/acprof:oso/9780195173444.001.0001

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A Local Linear Estimation Procedure for Functional Multilevel Modeling

A Local Linear Estimation Procedure for Functional Multilevel Modeling

Chapter:
(p.63) 3 A Local Linear Estimation Procedure for Functional Multilevel Modeling
Source:
Models for Intensive Longitudinal Data
Author(s):

Runze Li

Tammy L. Root

Saul Shiffman

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195173444.003.0003

Linear mixed models, also termed hierarchical linear models (HLM), have been particularly useful for researchers analyzing longitudinal data, but they are not appropriate for all types of longitudinal data. For example, these methods are not able to estimate changes in slope between an outcome variable and potentially time-varying covariates over time. The functional multilevel modeling technique proposed in this chapter addresses this issue by elaborating the linear mixed model to permit coefficients, both random and fixed, to vary nonparametrically over time. Estimation of time-varying coefficients is achieved by adding a local linear regression estimation procedure to the traditional linear mixed model. The main motivation for the current research was methodological challenges faced by drug-use researchers on how to model intensive longitudinal data.

Keywords:   mixed models, hierarchical linear models, longitudinal data, multilevel modeling, linear regression, substance abuse researchers

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