A quantile estimation for massive data with generalized Pareto distribution
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Jongwoo | - |
dc.contributor.author | Song, Seongjoo | - |
dc.date.accessioned | 2021-09-06T23:14:06Z | - |
dc.date.available | 2021-09-06T23:14:06Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2012-01-01 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/109109 | - |
dc.description.abstract | This paper proposes a new method of estimating extreme quantiles of heavy-tailed distributions for massive data. The method utilizes the Peak Over Threshold (POT) method with generalized Pareto distribution (GPD) that is commonly used to estimate extreme quantiles and the parameter estimation of GPD using the empirical distribution function (EDF) and nonlinear least squares (NLS). We first estimate the parameters of GPD using EDF and NLS and then, estimate multiple high quantiles for massive data based on observations over a certain threshold value using the conventional POT. The simulation results demonstrate that our parameter estimation method has a smaller Mean square error (MSE) than other common methods when the shape parameter of GPD is at least 0. The estimated quantiles also show the best performance in terms of root MSE (RMSE) and absolute relative bias (ARB) for heavy-tailed distributions. (C) 2011 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.title | A quantile estimation for massive data with generalized Pareto distribution | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Song, Seongjoo | - |
dc.identifier.doi | 10.1016/j.csda.2011.06.030 | - |
dc.identifier.scopusid | 2-s2.0-80052034714 | - |
dc.identifier.wosid | 000295436200012 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.56, no.1, pp.143 - 150 | - |
dc.relation.isPartOf | COMPUTATIONAL STATISTICS & DATA ANALYSIS | - |
dc.citation.title | COMPUTATIONAL STATISTICS & DATA ANALYSIS | - |
dc.citation.volume | 56 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 143 | - |
dc.citation.endPage | 150 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordAuthor | Quantile estimation | - |
dc.subject.keywordAuthor | Generalized Pareto distribution | - |
dc.subject.keywordAuthor | Peak over threshold | - |
dc.subject.keywordAuthor | Massive data | - |
dc.subject.keywordAuthor | Parameter estimation | - |
dc.subject.keywordAuthor | Nonlinear least squares | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.