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Article Dans Une Revue Computational Statistics and Data Analysis Année : 2007

Estimation and Inference in functional mixed-effects models

Résumé

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 general functional mixed-effects model. The usefulness of the proposed estimation and hypothesis testing procedures is illustrated by means of a small simulation study and a real-life dataset arising from physiology.

Dates et versions

hal-00171451 , version 1 (12-09-2007)

Identifiants

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Anestis Antoniadis, Theofanis Sapatinas. Estimation and Inference in functional mixed-effects models. Computational Statistics and Data Analysis, 2007, 51 (10), pp.4793-4813. ⟨10.1016/j.csda.2006.09.038⟩. ⟨hal-00171451⟩
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