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Estimation of Parameters in a Bivariate Generalized Exponential Distribution Based on Type-II Censored Samples
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Seong Wook | - |
| dc.contributor.author | Ng, Hon Keung Tony | - |
| dc.contributor.author | Jang, Hakjin | - |
| dc.date.accessioned | 2021-06-22T18:22:49Z | - |
| dc.date.available | 2021-06-22T18:22:49Z | - |
| dc.date.issued | 2016-01 | - |
| dc.identifier.issn | 0361-0918 | - |
| dc.identifier.issn | 1532-4141 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/16018 | - |
| dc.description.abstract | In this article, we discuss the maximum likelihood estimation and Bayesian estimation procedures for estimating the parameters in an absolute continuous bivariate generalized exponential distribution based on Type-II censored samples. A Markov chain Monte Carlo method is applied to compute the Bayes estimates. We also propose a method to obtain the initial estimates of the parameters for the required iterative algorithm. A simulation study is used to evaluate the performance of the proposed estimation procedures. Two real data examples are utilized to illustrate the methodology developed in this manuscript. © 2016, Copyright © Taylor & Francis Group, LLC. | - |
| dc.format.extent | 22 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Dekker | - |
| dc.title | Estimation of Parameters in a Bivariate Generalized Exponential Distribution Based on Type-II Censored Samples | - |
| dc.type | Article | - |
| dc.publisher.location | 미국 | - |
| dc.identifier.doi | 10.1080/03610918.2015.1130834 | - |
| dc.identifier.scopusid | 2-s2.0-84983001477 | - |
| dc.identifier.wosid | 000382581500018 | - |
| dc.identifier.bibliographicCitation | Communications in Statistics Part B: Simulation and Computation, v.45, no.10, pp 3776 - 3797 | - |
| dc.citation.title | Communications in Statistics Part B: Simulation and Computation | - |
| dc.citation.volume | 45 | - |
| dc.citation.number | 10 | - |
| dc.citation.startPage | 3776 | - |
| dc.citation.endPage | 3797 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Mathematics | - |
| dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
| dc.subject.keywordPlus | Algorithms | - |
| dc.subject.keywordPlus | Bayesian networks | - |
| dc.subject.keywordPlus | Intelligent systems | - |
| dc.subject.keywordPlus | Iterative methods | - |
| dc.subject.keywordPlus | Markov processes | - |
| dc.subject.keywordPlus | Maximum likelihood | - |
| dc.subject.keywordPlus | Maximum likelihood estimation | - |
| dc.subject.keywordPlus | Monte Carlo methods | - |
| dc.subject.keywordPlus | Numerical methods | - |
| dc.subject.keywordPlus | Bayesian estimations | - |
| dc.subject.keywordPlus | Dependence measures | - |
| dc.subject.keywordPlus | Estimation of parameters | - |
| dc.subject.keywordPlus | Estimation procedures | - |
| dc.subject.keywordPlus | Generalized exponential distribution | - |
| dc.subject.keywordPlus | Iterative algorithm | - |
| dc.subject.keywordPlus | Markov chain Monte Carlo method | - |
| dc.subject.keywordPlus | Simulation studies | - |
| dc.subject.keywordPlus | Parameter estimation | - |
| dc.subject.keywordAuthor | Bayesian estimation | - |
| dc.subject.keywordAuthor | Dependence measure | - |
| dc.subject.keywordAuthor | Maximum likelihood estimation | - |
| dc.subject.keywordAuthor | Monte Carlo simulation | - |
| dc.subject.keywordAuthor | Numerical method | - |
| dc.identifier.url | https://www.tandfonline.com/doi/full/10.1080/03610918.2015.1130834 | - |
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- Kim, Seong Wook
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