Robust joint modelling of sparsely observed paired functional data
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Le tri à trois modèles
les 1 000 travaux triés →1 modèle sur 3 a qualifié ce travail de métarecherche. Ce travail est contesté : il se situe à la frontière empirique du domaine, et son statut dépend du modèle interrogé. C'est l'un des 51 travaux du dossier des désaccords.
Develops and compares a robust statistical estimator for paired functional data, studying its properties via simulation against an existing method; borderline between statistical-methods research and pure domain statistics.
This develops a statistical model for functional data, not a study of how research is conducted.
Develops a robust statistical model for paired functional data (supernova light curves); domain methods development, not study of research.
Résumé
Abstract A reduced‐rank mixed‐effects model is developed for robust modelling of sparsely observed paired functional data. In this model, the curves for each functional variable are summarized using a few functional principal components, and the association of the two functional variables is modelled through the association of the principal component scores. A multivariate‐scale mixture of normal distributions is used to model the principal component scores and the measurement errors in order to handle outlying observations and achieve robust inference. The mean functions and principal component functions are modelled using splines, and roughness penalties are applied to avoid overfitting. An EM algorithm is developed for computation of model fitting and prediction. A simulation study shows that the proposed method outperforms an existing method, which is not designed for robust estimation. The effectiveness of the proposed method is illustrated through an application of fitting multiband light curves of Type Ia supernovae.
Conservé avec la notice de tri, où il sert de preuve aux étiquettes ci-dessus.
La notice
- Revue
- Canadian Journal of Statistics
- Thématique
- Statistical Methods and Inference
- Domaine
- Mathematics
- Établissements canadiens
- —
- Organismes subventionnaires
- —
- Mots-clés
- Functional principal component analysisOverfittingPrincipal component analysisFunctional data analysisComputer scienceMultivariate statisticsComputationRank (graph theory)MathematicsAlgorithmArtificial intelligenceMachine learningArtificial neural network
- Résumé présent dans OpenAlex
- oui