Estimation de la fonction de régression pour variable explicative et réponse fonctionnelles dépendantes

Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe

doi:10.1016/j.crma.2012.07.014

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…Condition (A1) is straight forward tailoring (in Ferraty et al, ( 2008)) of hypothesis (A1), when one substitutes semi-metric d(., .) by ℘(.…”

Section: Remarks On the Hypothesesmentioning

confidence: 99%

The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random

WAHİBA¹

2023

Econometrics

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The aim of this research was to study a nonparametric estimator of the density and mode function of a scalar response variable given a functional variable, when the observations are i.i.d. This proposed estimator is given by combining Missing At Random (MAR) with the local linear approach. Finally, a comparison study based on simulated data is also provided to illustrate the finite sample performances and the usefulness of the local linear approach with MAR to the presence of even a small proportion of outliers in the data.

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“…Condition (A1) is straight forward tailoring (in Ferraty et al, ( 2008)) of hypothesis (A1), when one substitutes semi-metric d(., .) by ℘(.…”

Section: Remarks On the Hypothesesmentioning

confidence: 99%

The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random

WAHİBA¹

2023

Econometrics

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“…It plays an important role in statistics and arises in a variety of fields, including econometrics, epidemiology, environmental science, and many others. The work in [3] is an important contribution to the conditional hazard rate for functional covariates in an infinite-dimensional space. Censored data are a type of data in which the values are incomplete or partially known.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Conditional Hazard Function with a Recursive Kernel from Censored Functional Ergodic Data

Kebir

Mechab

2023

Iocma 2023

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“…A wide range of literature in this field is provided by Tanner and Wong (1983), Singpurwalla and Wong (1983), Hassani and al. (1986) or by Ferraty and al. (2008).…”

Section: Introductionmentioning

confidence: 99%

On the strong convergence of the hazard rate and its maximum risk point estimators in presence of censorship and functional explanatory covariate

Agbokou¹,

Gneyou²

2017

JAS

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Abstract. . In the literature much work has been devoted to the non-parametric estimation of survival analysis functions. In this work, we focus on the nonparametric estimation of the conditional hazard rate and the point of its maximum, in the model of right censored data with presence of functional covariate. We establish the almost uniform complete convergence of these estimators at appropriate rates. This generalizes the almost sure convergence obtained in the literature.Résumé. Dans la littérature beaucoup de travaux ontété consacrésà l'estimation nonparamétrique des fonctions d'analyse de survie. Dans ce travail, nous nous focalisons sur l'estimation non-paramétrique du taux de hasard conditionnel et du point de son maximum, dans le modèle des données censuréesà droite et en présence de covariable fonctionnelle. Nousétablissons la convergence uniforme presque complète de ces estimateursà des vitesses appropriées. Ce qui généralise la convergence presque sûre obtenue dans la littérature.

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Estimation de la fonction de régression pour variable explicative et réponse fonctionnelles dépendantes

Cited by 9 publications

References 8 publications

The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random

The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random

Estimation of the Conditional Hazard Function with a Recursive Kernel from Censored Functional Ergodic Data

On the strong convergence of the hazard rate and its maximum risk point estimators in presence of censorship and functional explanatory covariate

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