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Estimation non-paramétrique dans un modèle de censure.

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dc.contributor.author El-Hadj Ali, Thouria
dc.contributor.author Messaci, Fatiha
dc.contributor.author Bouzebda, Salim
dc.date.accessioned 2022-05-25T08:46:18Z
dc.date.available 2022-05-25T08:46:18Z
dc.date.issued 2021-07-01
dc.identifier.uri http://depot.umc.edu.dz/handle/123456789/8900
dc.description.abstract In this thesis, we are concerned with the uniform in bandwidth consistency of kernel-type estimators of the regression function derived by modern empirical process theory, under weaker conditions on the kernel than previously used in the literature. Our theorems allow data-driven local bandwidths for these statistics. We extend existing uniform bounds on kernel type-estimator and making it adaptive to the intrinsic dimension of the underlying distribution, which will be characterising by the so-called intrinsic dimension. The thesis is divided in three main parts, we describe as follows. The first part is devoted to general empirical processes indexed by classes of functions. The results are obtained for uniformly bounded classes of functions or unbounded with envelope functions satisfying some moment conditions. The purpose of the second part is the statistical applications to illustrate the usefullness of the main contribution. Applications include the uniform in bandwidth consistency of the kernel type estimators for density, regression, the conditional distribution, multivariate mode, Shannon’s entropy, derivatives of density and regression functions. The third part is devoted to the uniform in bandwidth consistency for non-parametric inverse probability of censoring weighted (I.P.C.W.) estimators of the regression function under random censorship. These new results are applied for the non-parametric conditional density and conditional distribution functions.
dc.language.iso fr
dc.publisher Université Frères Mentouri - Constantine 1
dc.subject Mathematiques: Probabilités et Statistique
dc.subject Processus empiriques conditionnels
dc.subject classes VC
dc.subject l’estimateur à noyau
dc.subject fonction de densité
dc.subject fonction de régression
dc.subject données censurées
dc.subject Conditional empirical processes
dc.subject VC-classes
dc.subject Kernel-type estimators
dc.subject density function
dc.subject regression function
dc.subject censored data
dc.subject العمليات التجريبية الشرطية
dc.subject الفئات من نوعVC
dc.subject المقدرات من نوع النواة
dc.subject دالة الكثافة
dc.subject دالة الانحدار
dc.title Estimation non-paramétrique dans un modèle de censure.
dc.type Thesis

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