Low-storage quantile estimation
type: Item
instance of: scholarly article
title: Low-storage quantile estimation
zbMATH Open document ID: 0938.62032
author: C. Hurley
author: Reza Modarres
published in: Computational Statistics
publication date: 2000-03-02T00:00:00Z
review text: Many different approaches have been taken to the problem of low-storage quantile estimation. One approach is based on stochastic approximation. Others, such as minimax and recursive median, are based on trees. The authors compare their computational properties such as storage demands, the ease and speed of computation, as well as statistical properties such as asymptotic behavior and robustness. An approach to low-storage quantile estimation based on the sample histogram, which has good computational and statistical properties, is then described. A simulation study is presented where the efficiency of various low-storage quantile estimators are compared. It is shown that quantile estimates based on the sample histogram are similar to the sample quantile and have greater accuracy than the estimates produced by other more complex and computationally intensive methods.
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 65Y20
zbMATH DE Number: 1409705
zbMATH Keywords: histogram
zbMATH Keywords: quantile estimates
zbMATH Keywords: efficiency
reviewed by: M. P. Moklyachuk
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 1409705