Estimation and inference in functional mixed-effects models

Functional mixed-effects models are very useful in analyzing functional data. A general functional mixed-effects model that inherits the flexibility of linear mixed-effects models in handling complex designs and correlation structures is considered. A wavelet decomposition approach is used to model both fixed-effects and random-effects in the same functional space, meaning that the population-average curve and the subject-specific curves have the same smoothness property.A linear mixed-effects representation is then obtained that is used for estimation and inference in the general functional mixed-effects model.Adapting recent methodologies in linear mixed-effects and nonparametric regression models, hypothesis testing procedures for both fixed-effects and random-effects are provided. Using classical linear mixed-effects estimation techniques, the linear mixed-effects representation is also used to obtain wavelet-based estimates for both fixed-effects and random-effects in the ge...

• Type: Journal
• Year: 2007
• Where: CSDA
• Authors: Anestis Antoniadis, Theofanis Sapatinas
• Keywords: CSDA 2007, Functional Mixed-effects Model, General Functional Mixed-effects, Linear