Regression imputation in the functional linear model with missing values in the response - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue Journal of Statistical Planning and Inference Année : 2019

Regression imputation in the functional linear model with missing values in the response

Résumé

We are interested in functional linear regression when some observations of the real response are missing, while the functional covariate is completely observed. A complete case regression imputation method of missing data is presented, using functional principal component regression to estimate the functional coefficient of the model. We study the asymptotic behaviour of the error when the missing data is replaced by the regression imputed value, in a 'missing at random' framework. The completed database can be used to estimate the functional coefficient of the model and to predict new values of the response. The practical behaviour of the method is also studied on simulated data sets. A real dataset study is performed in the environmental context of air quality.
Fichier principal
Vignette du fichier
elsarticle-template.pdf (1.41 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01521954 , version 1 (12-05-2017)
hal-01521954 , version 2 (30-05-2017)
hal-01521954 , version 3 (03-10-2017)
hal-01521954 , version 4 (09-05-2018)

Identifiants

Citer

Christophe Crambes, Yousri Henchiri. Regression imputation in the functional linear model with missing values in the response. Journal of Statistical Planning and Inference, 2019, 201, pp.103-109. ⟨10.1016/j.jspi.2018.12.004⟩. ⟨hal-01521954v4⟩
297 Consultations
1003 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More