Abstract
AbstractFunctional 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. WIREs Comput Stat 2012, 4:527–534. doi: 10.1002/wics.1226This article is categorized under: Statistical Models > Classification Models
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