Bayesian Shrinkage for Functional Network Models, With Applications to Longitudinal Item Response Data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jaewoo Park | - |
dc.contributor.author | YESEULJEON | - |
dc.contributor.author | Minsuk Shin | - |
dc.contributor.author | Minjeong Jeon | - |
dc.contributor.author | Ick Hoon Jin | - |
dc.date.accessioned | 2022-05-20T06:40:08Z | - |
dc.date.available | 2022-05-20T06:40:08Z | - |
dc.date.issued | 2022-05 | - |
dc.identifier.issn | 1061-8600 | - |
dc.identifier.uri | https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6295 | - |
dc.description.abstract | Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is crucial for educational assessment or designing marketing strategies from survey questions. Although dynamic network models have been widely developed, we cannot apply them directly to item response data because there are multiple systems of nodes with various types of local interactions among items, resulting in multiplex network structures. We propose a new model to study these temporal interactions among items by embedding the functional parameters within the exponential random graph model framework. Inference on such models is difficult because the likelihood functions contain intractable normalizing constants. Furthermore, the number of functional parameters grows exponentially as the number of items increases. Variable selection for such models is not trivial because standard shrinkage approaches do not consider temporal trends in functional parameters. To overcome these challenges, we develop a novel Bayes approach by combining an auxiliary variable MCMC algorithm and a recently developed functional shrinkage method. We apply our algorithm to survey and review datasets, illustrating that the proposed approach can avoid the evaluation of intractable normalizing constants as well as the detection of significant temporal interactions among items. Through a simulation study under different scenarios, we examine the performance of our algorithm. Our method is, to our knowledge, the first attempt to select functional variables for models with intractable normalizing constants. Supplementary materials for this article are available online. | - |
dc.format.extent | 18 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | AMER STATISTICAL ASSOC | - |
dc.title | Bayesian Shrinkage for Functional Network Models, With Applications to Longitudinal Item Response Data | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1080/10618600.2021.1999823 | - |
dc.identifier.bibliographicCitation | JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, v.31, no.2, pp 360 - 377 | - |
dc.citation.title | JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS | - |
dc.citation.volume | 31 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 360 | - |
dc.citation.endPage | 377 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Bayesian functional shrinkage | - |
dc.subject.keywordAuthor | Doubly intractable distributions | - |
dc.subject.keywordAuthor | Exponential random graph model | - |
dc.subject.keywordAuthor | Ising graphical model | - |
dc.subject.keywordAuthor | Longitudinal networks | - |
Items in Scholar Hub are protected by copyright, with all rights reserved, unless otherwise indicated.
Yonsei University 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea1599-1885
© 2021 YONSEI UNIV. 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.