A Local Linear Estimation Procedure for Functional Multilevel Modeling

Runze Li, Tammy L. Root, Saul Shiffman

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationModels for Intensive Longitudinal Data
PublisherOxford University Press
ISBN (Electronic)9780199847051
ISBN (Print)9780195173444
StatePublished - Mar 22 2012

All Science Journal Classification (ASJC) codes

  • Psychology(all)


Dive into the research topics of 'A Local Linear Estimation Procedure for Functional Multilevel Modeling'. Together they form a unique fingerprint.

Cite this