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CSDA
2007

Estimation and inference in functional mixed-effects models

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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...
Anestis Antoniadis, Theofanis Sapatinas
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where CSDA
Authors Anestis Antoniadis, Theofanis Sapatinas
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