Local linear estimate of the nonparametric robust regression in functional data

2018 ◽  
Vol 134 ◽  
pp. 128-133 ◽  
Author(s):  
Faiza Belarbi ◽  
Souheyla Chemikh ◽  
Ali Laksaci
Author(s):  
Oussama Bouanani ◽  
Abdelhak Guendouzi ◽  
Souheyla Chemikh

In this work, we treat a prediction problem via the conditional hazard function of a scalar response variable Y given a functional random variable X by using the local linear technique. The main purpose of this paper is to investigate the asymptotic normality of the nonparametric estimator of the conditional hazard function, under some general conditions. A simulation study, conducted to assess finite sample behavior, demonstrates the superiority of our method than the standard kernel method


2017 ◽  
Vol 11 (4) ◽  
pp. 771-789 ◽  
Author(s):  
Abdelkader Chahad ◽  
Larbi Ait-Hennani ◽  
Ali Laksaci

Statistics ◽  
2013 ◽  
Vol 47 (1) ◽  
pp. 26-44 ◽  
Author(s):  
Jacques Demongeot ◽  
Ali Laksaci ◽  
Fethi Madani ◽  
Mustapha Rachdi

2018 ◽  
Vol 28 (2) ◽  
pp. 217-240 ◽  
Author(s):  
Fahimah A. Al-Awadhi ◽  
Zoulikha Kaid ◽  
Ali Laksaci ◽  
Idir Ouassou ◽  
Mustapha Rachdi

Author(s):  
Sara Leulmi ◽  
Fatiha Messaci

We introduce a local linear nonparametric estimation for the generalized regression function of a scalar response variable given a random variable taking values in a semi metric space. We establish a rate of uniform consistency for the proposed estimators. Then, based on a real data set we illustrate the performance of a particular studied estimator with respect to other known estimators


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