Prediction in function-on-function linear model with partially observed functional covariate and response - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

Prediction in function-on-function linear model with partially observed functional covariate and response

Résumé

In this work, we are interested in a function-on-function linear model in which the response and the covariate are partially observed curves. First, we reconstruct the missing part of the covariate using the observed parts. Then, we consider two strategies for dealing with the missing part of the response. The first one consists in a reconstruction in the same way as for the covariate. The second one uses regression imputation. Once the dataset is reconstructed, we estimate the slope function and give the mean square prediction error for a new observation of the covariate. Both methods are compared from a theoretical and a practical point of view.
Fichier principal
Vignette du fichier
article_VF.pdf (5.79 Mo) Télécharger le fichier

Dates et versions

hal-04145914 , version 1 (11-07-2023)

Identifiants

  • HAL Id : hal-04145914 , version 1

Citer

Christophe Crambes, Chayma Daayeb, Ali Gannoun, Yousri Henchiri. Prediction in function-on-function linear model with partially observed functional covariate and response. 2023. ⟨hal-04145914⟩
26 Consultations
19 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More