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Hauptverfasser: Alem, Mohamed Kaber El, Guessoum, Zohra, Tatachak, Abdelkader, Sadki, Ourida
Format: Preprint
Veröffentlicht: 2025
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2509.15353
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author Alem, Mohamed Kaber El
Guessoum, Zohra
Tatachak, Abdelkader
Sadki, Ourida
author_facet Alem, Mohamed Kaber El
Guessoum, Zohra
Tatachak, Abdelkader
Sadki, Ourida
contents In this paper, we establish the convergence rate in central limit theorem (CLT) for linearly extended negative quadrant dependent (LENQD) random variables (rv's). Under some weak conditions, the rate of normal approximation is shown as $O(n^{-1/9})$. As an application, the convergence rate in CLT of the wavelet estimator for the nonparametric regression model with LENQD errors is presented as $O(n^{-1/9})$. The performance of the main results is illustrated through a simulation study based on a real dataset.
format Preprint
id arxiv_https___arxiv_org_abs_2509_15353
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle On the convergence rate in the central limit theorem for linearly extended negative quadrant dependent random variables and its applications
Alem, Mohamed Kaber El
Guessoum, Zohra
Tatachak, Abdelkader
Sadki, Ourida
Statistics Theory
Primary 60F05, Secondary 62G20
G.3
In this paper, we establish the convergence rate in central limit theorem (CLT) for linearly extended negative quadrant dependent (LENQD) random variables (rv's). Under some weak conditions, the rate of normal approximation is shown as $O(n^{-1/9})$. As an application, the convergence rate in CLT of the wavelet estimator for the nonparametric regression model with LENQD errors is presented as $O(n^{-1/9})$. The performance of the main results is illustrated through a simulation study based on a real dataset.
title On the convergence rate in the central limit theorem for linearly extended negative quadrant dependent random variables and its applications
topic Statistics Theory
Primary 60F05, Secondary 62G20
G.3
url https://arxiv.org/abs/2509.15353