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Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples

In the present article, by utilizing some inequalities for linearly negative quadrant dependent random variables, we discuss the uniformly asymptotic normality of sample quantiles for linearly negative quadrant dependent samples under mild conditions. The rate of uniform asymptotic normality is pres...

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Detalles Bibliográficos
Autores principales: Hu, Xueping, Jiang, Rong, Yu, Keming, Zhang, Tong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096958/
https://www.ncbi.nlm.nih.gov/pubmed/30839555
http://dx.doi.org/10.1186/s13660-018-1788-6
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author Hu, Xueping
Jiang, Rong
Yu, Keming
Zhang, Tong
author_facet Hu, Xueping
Jiang, Rong
Yu, Keming
Zhang, Tong
author_sort Hu, Xueping
collection PubMed
description In the present article, by utilizing some inequalities for linearly negative quadrant dependent random variables, we discuss the uniformly asymptotic normality of sample quantiles for linearly negative quadrant dependent samples under mild conditions. The rate of uniform asymptotic normality is presented and the rate of convergence is near [Formula: see text] when the third moment is finite, which extends and improves the corresponding results of Yang et al. (J. Inequal. Appl. 2011:83, 2011) and Liu et al. (J. Inequal. Appl. 2014:79, 2014) under negatively associated random samples in some sense.
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spelling pubmed-60969582018-08-24 Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples Hu, Xueping Jiang, Rong Yu, Keming Zhang, Tong J Inequal Appl Research In the present article, by utilizing some inequalities for linearly negative quadrant dependent random variables, we discuss the uniformly asymptotic normality of sample quantiles for linearly negative quadrant dependent samples under mild conditions. The rate of uniform asymptotic normality is presented and the rate of convergence is near [Formula: see text] when the third moment is finite, which extends and improves the corresponding results of Yang et al. (J. Inequal. Appl. 2011:83, 2011) and Liu et al. (J. Inequal. Appl. 2014:79, 2014) under negatively associated random samples in some sense. Springer International Publishing 2018-07-28 2018 /pmc/articles/PMC6096958/ /pubmed/30839555 http://dx.doi.org/10.1186/s13660-018-1788-6 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Hu, Xueping
Jiang, Rong
Yu, Keming
Zhang, Tong
Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples
title Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples
title_full Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples
title_fullStr Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples
title_full_unstemmed Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples
title_short Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples
title_sort uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096958/
https://www.ncbi.nlm.nih.gov/pubmed/30839555
http://dx.doi.org/10.1186/s13660-018-1788-6
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