基于弱相依且随机左截断数据的核密度估计的一个贝里 - 埃森型界。
A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data.
作者信息
Asghari Petros, Fakoor Vahid
机构信息
Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P.O. Box 1159-91775, Mashhad, Iran.
出版信息
J Inequal Appl. 2017;2017(1):1. doi: 10.1186/s13660-016-1272-0. Epub 2017 Jan 3.
Abstract
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.
摘要
在许多应用中，可用数据来自一种抽样方案，该方案会导致在左截断方面的信息损失。在某些情况下，除了左截断之外，数据还具有弱相依性。在本文中，我们感兴趣的是推导左截断且弱相依数据的核密度估计量的渐近正态性以及一个贝里 - 埃森型界。
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