title: A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data.
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.
authors.affiliation: Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P.O. Box 1159-91775, Mashhad, Iran.
authors.forename: Petros
authors.initials: P
authors.lastname: Asghari
authors.affiliation: Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P.O. Box 1159-91775, Mashhad, Iran.
authors.forename: Vahid
authors.initials: V
authors.lastname: Fakoor
keywords: Berry-Esseen; asymptotic normality; left-truncation; weakly dependent; α-mixing