Bootstrap inference for network vector autoregression in large-scale social network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Manho | - |
dc.contributor.author | Hwang, Eunju | - |
dc.date.accessioned | 2021-12-05T02:40:08Z | - |
dc.date.available | 2021-12-05T02:40:08Z | - |
dc.date.created | 2021-03-29 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.issn | 1226-3192 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82843 | - |
dc.description.abstract | A large amount of online social network data such as Facebook or Twitter are extensively generated by the growth of social network platforms in recent years. Development of a network time series model and its statistical inference are as important as the rapid progress on the social network technology and evolution. In this work we consider a network vector autoregression for the large-scale social network, proposed by Zhu et al. (Ann Stat 45(3):1096-1123, 2017), and study its bootstrap estimation and bootstrap forecast. In order to suggest a bootstrap version of parameter estimates in the underlying model, two bootstrap methods are combined together: stationary bootstrap and classical residual bootstrap. Consistency of the bootstrap estimator is established and the bootstrap confidence intervals are constructed. Moreover, we obtain bootstrap prediction intervals for multi-step ahead future values. A Monte-Carlo study illustrates better finite-sample performances of our bootstrap technique than those by the standard method. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER HEIDELBERG | - |
dc.relation.isPartOf | JOURNAL OF THE KOREAN STATISTICAL SOCIETY | - |
dc.title | Bootstrap inference for network vector autoregression in large-scale social network | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000630832200001 | - |
dc.identifier.doi | 10.1007/s42952-021-00115-7 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.50, no.4, pp.1238 - 1258 | - |
dc.identifier.kciid | ART002786875 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85103091371 | - |
dc.citation.endPage | 1258 | - |
dc.citation.startPage | 1238 | - |
dc.citation.title | JOURNAL OF THE KOREAN STATISTICAL SOCIETY | - |
dc.citation.volume | 50 | - |
dc.citation.number | 4 | - |
dc.contributor.affiliatedAuthor | Hong, Manho | - |
dc.contributor.affiliatedAuthor | Hwang, Eunju | - |
dc.type.docType | Article; Early Access | - |
dc.subject.keywordAuthor | Network vector autoregression | - |
dc.subject.keywordAuthor | Stationary bootstrap | - |
dc.subject.keywordAuthor | Residual bootstrap | - |
dc.subject.keywordAuthor | Prediction intervals | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.description.journalRegisteredClass | other | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon 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.