Universal asymptotic normality for conditionally trimmed sums
type: Item
instance of: scholarly article
title: Universal asymptotic normality for conditionally trimmed sums
zbMATH Open document ID: 0662.62014
DOI: 10.1016/0167-7152(88)90079-X
published in: Statistics \& Probability Letters
publication date: 1988-01-01T00:00:00Z
review text: The conditionally trimmed sums formed from an arbitrary i.i.d. sample are shown to satisfy both a probabilistic and empirical central limit theorem with a normal limit law. The specific method of trimming attempts to retain as many summands as possible and deletes only terms of sufficient magnitude. The behavior of the deleted terms is also studied for random variables which generate affinely stochastically compact partial sums.
Mathematics Subject Classification ID: 62E20
zbMATH DE Number: 4082710
zbMATH Keywords: asymptotic normality
zbMATH Keywords: extreme values
zbMATH Keywords: conditionally trimmed sums
zbMATH Keywords: central limit theorem
zbMATH Keywords: normal limit law
author: Marjorie G. Hahn
author: Jim Kuelbs
MaRDI profile type: Publication
cites work: Q3906818
cites work: What portion of the sample makes a partial sum asymptotically stable or normal?
cites work: Q5616553
cites work: Q5512461
cites work: Asymptotic normality of trimmed sums of \(\Phi\)-mixing random variables
cites work: Extreme values and a Gaussian central limit theorem
cites work: The asymptotic distribution of the trimmed mean
full work available at URL: 0167-7152(88)90079-x
OpenAlex ID: W1988966824
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description: scientific article; zbMATH DE number 4082710