Nonparametric estimation of the conditional distribution function in a semiparametric censorship model

2008 ◽  
Vol 138 (10) ◽  
pp. 3044-3058 ◽  
Author(s):  
Maria Carmen Iglesias-Pérez ◽  
Jacobo de Uña-Álvarez
Keyword(s):  
Distribution Function ◽  
Nonparametric Estimation ◽  
Conditional Distribution ◽  
Conditional Distribution Function

scholarly journals Asymptotic normality of conditional distribution estimation in the single index model

2017 ◽  
Vol 9 (1) ◽  
pp. 162-175
Author(s):  
Diaa Eddine Hamdaoui ◽  
Amina Angelika Bouchentouf ◽  
Abbes Rabhi ◽  
Toufik Guendouzi
Keyword(s):  
Distribution Function ◽  
Confidence Interval ◽  
Asymptotic Normality ◽  
Conditional Distribution ◽  
Conditional Distribution Function ◽  
Single Index ◽  
Index Model ◽  
Single Index Model ◽  
Distribution Estimation

AbstractThis paper deals with the estimation of conditional distribution function based on the single-index model. The asymptotic normality of the conditional distribution estimator is established. Moreover, as an application, the asymptotic (1 − γ) confidence interval of the conditional distribution function is given for 0 < γ < 1.

scholarly journals Smooth kNN Local Linear Estimation of the Conditional Distribution Function

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1102
Author(s):  
Ibrahim M. Almanjahie ◽  
Zouaoui Chikr Elmezouar ◽  
Ali Laksaci ◽  
Mustapha Rachdi
Keyword(s):  
Distribution Function ◽  
Conditional Distribution ◽  
Linear Estimation ◽  
Mixing Conditions ◽  
K Nearest Neighbors ◽  
Conditional Distribution Function ◽  
Times Series ◽  
Local Linear ◽  
Cumulative Function ◽  
Local Linear Estimation

Previous works were dedicated to the functional k-Nearest Neighbors (kNN) and the local linearity method estimations of a regression operator. In this paper, a sequence pair of (Xi,Yi)i=1,…,n of functional mixing observations are considered. We treat the local linear estimation of the cumulative function of Yi given functional input variable Xi. Precisely, we combine the kNN method with the local linear algorithm to construct a new and fast efficiency estimator of the conditional distribution function. The main purpose of this paper is to prove the strong convergence of the constructed estimator under mixing conditions. An application to the functional times series prediction is used to compare our proposed estimator with the existing competitive estimators, and show its efficiency and superiority.

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