Functional mixed effects models

Ziyue Liu, Wensheng Guo

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Functional mixed effects model (FMM) is a mixed effects modeling framework that both the fixed effects and the random effects are modeled by nonparametric curves. The combination of mixed effects model and nonparametric smoothing enables FMMs to handle outcomes with complex profiles and at the same time to incorporate complex experimental designs and include covariates. Estimation and inference can be performed either using techniques from linear mixed effects models or using fully Bayesian approaches. As in functional data analysis, inference in FMMs is preliminary and needs to be further investigated. Several software packages have been developed to implement FMMs, although computational challenges do exist no matter which smoothing method is used.

Original languageEnglish
Pages (from-to)527-534
Number of pages8
JournalWiley Interdisciplinary Reviews: Computational Statistics
Volume4
Issue number6
DOIs
StatePublished - Nov 2012

Fingerprint

Mixed Effects Model
Nonparametric Smoothing
Linear Mixed Effects Model
Functional Data Analysis
Mixed Effects
Smoothing Methods
Fixed Effects
Random Effects
Experimental design
Bayesian Approach
Software Package
Covariates
Curve
Modeling
Profile
Framework

Keywords

  • Functional data analysis
  • Mixed effects
  • Nonparametric smoothing

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Functional mixed effects models. / Liu, Ziyue; Guo, Wensheng.

In: Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 4, No. 6, 11.2012, p. 527-534.

Research output: Contribution to journalArticle

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