Threshold estimation of generalized pareto distribution based on akaike information criterion for accurate reliability analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, S. | - |
dc.contributor.author | Lim, W. | - |
dc.contributor.author | Cho, S.-G. | - |
dc.contributor.author | Park, S. | - |
dc.contributor.author | Lee, M. | - |
dc.contributor.author | Choi, J.-S. | - |
dc.contributor.author | Hong, S. | - |
dc.contributor.author | Lee, T.H. | - |
dc.date.accessioned | 2021-08-03T04:45:17Z | - |
dc.date.available | 2021-08-03T04:45:17Z | - |
dc.date.issued | 2015 | - |
dc.identifier.issn | 1226-4873 | - |
dc.identifier.uri | https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/836 | - |
dc.description.abstract | In order to perform estimations with high reliability, it is necessary to deal with the tail part of the cumulative distribution function (CDF) in greater detail compared to an overall CDF. The use of a generalized Pareto distribution (GPD) to model the tail part of a CDF is receiving more research attention with the goal of performing estimations with high reliability. Current studies on GPDs focus on ways to determine the appropriate number of sample points and their parameters. However, even if a proper estimation is made, it can be inaccurate as a result of an incorrect threshold value. Therefore, in this paper, a GPD based on the Akaike information criterion (AIC) is proposed to improve the accuracy of the tail model. The proposed method determines an accurate threshold value using the AIC with the overall samples before estimating the GPD over the threshold. To validate the accuracy of the method, its reliability is compared with that obtained using a general GPD model with an empirical CDF. ? 2015 The Korean Society of Mechanical Engineers. | - |
dc.format.extent | 6 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | Korean Society of Mechanical Engineers | - |
dc.title | Threshold estimation of generalized pareto distribution based on akaike information criterion for accurate reliability analysis | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.3795/KSME-A.2015.39.2.163 | - |
dc.identifier.scopusid | 2-s2.0-84938408130 | - |
dc.identifier.bibliographicCitation | Transactions of the Korean Society of Mechanical Engineers, A, v.39, no.2, pp 163 - 168 | - |
dc.citation.title | Transactions of the Korean Society of Mechanical Engineers, A | - |
dc.citation.volume | 39 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 163 | - |
dc.citation.endPage | 168 | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART001958059 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordPlus | Distribution functions | - |
dc.subject.keywordPlus | Estimation | - |
dc.subject.keywordPlus | Pareto principle | - |
dc.subject.keywordPlus | Reliability | - |
dc.subject.keywordPlus | Akaike information criterion | - |
dc.subject.keywordPlus | Cumulative distribution function | - |
dc.subject.keywordPlus | Generalized Pareto distribution | - |
dc.subject.keywordPlus | Generalized Pareto Distributions | - |
dc.subject.keywordPlus | High reliability | - |
dc.subject.keywordPlus | Number of samples | - |
dc.subject.keywordPlus | Threshold | - |
dc.subject.keywordPlus | Threshold estimation | - |
dc.subject.keywordPlus | Reliability analysis | - |
dc.subject.keywordAuthor | Akaike Information Criterion | - |
dc.subject.keywordAuthor | Generalized Pareto Distribution | - |
dc.subject.keywordAuthor | Reliability Analysis | - |
dc.subject.keywordAuthor | Tail Model | - |
dc.subject.keywordAuthor | Threshold | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(34103) 대전광역시 유성구 유성대로1312번길 32042-866-3114
COPYRIGHT 2021 BY KOREA RESEARCH INSTITUTE OF SHIPS & OCEAN ENGINEERING. 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.