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
Descripción
Sumario: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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