On non parametric kernel estimation of the mode of the regression function in the strong mixing random design model with censored data-Reference-Cited by-同舟云学术

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On non parametric kernel estimation of the mode of the regression function in the strong mixing random design model with censored data

Published:2024-07-29 Issue: Volume: Page:1-34
ISSN:0361-0926
Container-title:Communications in Statistics - Theory and Methods
language:en
Short-container-title:Communications in Statistics - Theory and Methods

Author:

Bouzebda Salim¹,Khardani Salah²,Slaoui Yousri³

Affiliation:

1. Université de technologie de Compiègne, LMAC (Laboratory of Applied Mathematics of Compiègne), Compiègne, France

2. Université El-Manar, Faculté des Sciences Tunis, Laboratoire de Modélisation Mathématique, Analyse Harmonique, Théorie de potentiel, Tunis, Tunisia

3. Université de Poitiers, LMA (Laboratoire de Mathématiques et Applications), Poitiers, France

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610926.2024.2372062

Reference80 articles.

1. An approximation to the density function

2. Uniform almost sure convergence and asymptotic distribution of the wavelet-based estimators of partial derivatives of multivariate density function under weak dependence

3. Andersen, P. K.O. R. Borgan, R. D. Gill, and N. Keiding. 1993. Statistical models based on counting processes. In Springer Series in Statistics. New York: Springer-Verlag.

4. Normalité asymptotique d'estimateurs convergents du mode conditionnel

5. Billingsley, P. 1968. Convergence of probability measures. New York-London-Sydney: John Wiley & Sons, Inc.

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