A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data

In many applications, the available data come from a sampling scheme that causes loss of information in terms of left truncation. In some cases, in addition to left truncation, the data are weakly dependent. In this paper we are interested in deriving the asymptotic normality as well as a Berry-Esse...

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Detalles Bibliográficos
Autores principales: Asghari, Petros, Fakoor, Vahid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5209448/
https://www.ncbi.nlm.nih.gov/pubmed/28111501
http://dx.doi.org/10.1186/s13660-016-1272-0
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author Asghari, Petros
Fakoor, Vahid
author_facet Asghari, Petros
Fakoor, Vahid
author_sort Asghari, Petros
collection PubMed
description In many applications, the available data come from a sampling scheme that causes loss of information in terms of left truncation. In some cases, in addition to left truncation, the data are weakly dependent. In this paper we are interested in deriving the asymptotic normality as well as a Berry-Esseen type bound for the kernel density estimator of left truncated and weakly dependent data.
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spelling pubmed-52094482017-01-18 A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data Asghari, Petros Fakoor, Vahid J Inequal Appl Research In many applications, the available data come from a sampling scheme that causes loss of information in terms of left truncation. In some cases, in addition to left truncation, the data are weakly dependent. In this paper we are interested in deriving the asymptotic normality as well as a Berry-Esseen type bound for the kernel density estimator of left truncated and weakly dependent data. Springer International Publishing 2017-01-03 2017 /pmc/articles/PMC5209448/ /pubmed/28111501 http://dx.doi.org/10.1186/s13660-016-1272-0 Text en © The Author(s) 2017 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
Asghari, Petros
Fakoor, Vahid
A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data
title A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data
title_full A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data
title_fullStr A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data
title_full_unstemmed A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data
title_short A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data
title_sort berry-esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5209448/
https://www.ncbi.nlm.nih.gov/pubmed/28111501
http://dx.doi.org/10.1186/s13660-016-1272-0
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