Academic Journal
A composite likelihood approach to gaussian network differentiation with application to epigenetics.
| Τίτλος: | A composite likelihood approach to gaussian network differentiation with application to epigenetics. |
|---|---|
| Συγγραφείς: | Adefisoye, James O.1,2 (AUTHOR), Arshad, S. Hasan3,4 (AUTHOR), Zhang, Hongmei1 (AUTHOR) hzhang6@memphis.edu |
| Πηγή: | Journal of Applied Statistics. Sep2026, Vol. 53 Issue 12, p2422-2440. 19p. |
| Θεματικοί όροι: | *Simulation methods & models, Epigenetics, Dependence (Statistics) |
| Περίληψη: | For networks originated from dependent populations, methods to test network differentiation between the two populations are generally designed incorporating the nature of dependence. Doing so potentially complicates the inferencing process with heavy computing burden. Through simulations, we assess the value of using composite likelihood to carry out network comparisons under different statuses of population dependency. We apply the method to real-life epigenetic data and assess epigenetic network stability over time. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Applied Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:bsx&genre=article&issn=02664763&ISBN=&volume=53&issue=12&date=20260915&spage=2422&pages=2422-2440&title=Journal of Applied Statistics&atitle=A%20composite%20likelihood%20approach%20to%20gaussian%20network%20differentiation%20with%20application%20to%20epigenetics.&aulast=Adefisoye%2C%20James%20O.&id=DOI:10.1080/02664763.2025.2596185 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: bsx DbLabel: Business Source Index An: 196828200 RelevancyScore: 1435 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1435 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A composite likelihood approach to gaussian network differentiation with application to epigenetics. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Adefisoye%2C+James+O%2E%22">Adefisoye, James O.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Arshad%2C+S%2E+Hasan%22">Arshad, S. Hasan</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hongmei%22">Zhang, Hongmei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hzhang6@memphis.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Statistics%22">Journal of Applied Statistics</searchLink>. Sep2026, Vol. 53 Issue 12, p2422-2440. 19p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Epigenetics%22">Epigenetics</searchLink><br /><searchLink fieldCode="DE" term="%22Dependence+%28Statistics%29%22">Dependence (Statistics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: For networks originated from dependent populations, methods to test network differentiation between the two populations are generally designed incorporating the nature of dependence. Doing so potentially complicates the inferencing process with heavy computing burden. Through simulations, we assess the value of using composite likelihood to carry out network comparisons under different statuses of population dependency. We apply the method to real-life epigenetic data and assess epigenetic network stability over time. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Applied Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsx&AN=196828200 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/02664763.2025.2596185 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 2422 Subjects: – SubjectFull: Simulation methods & models Type: general – SubjectFull: Epigenetics Type: general – SubjectFull: Dependence (Statistics) Type: general Titles: – TitleFull: A composite likelihood approach to gaussian network differentiation with application to epigenetics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Adefisoye, James O. – PersonEntity: Name: NameFull: Arshad, S. Hasan – PersonEntity: Name: NameFull: Zhang, Hongmei IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02664763 Numbering: – Type: volume Value: 53 – Type: issue Value: 12 Titles: – TitleFull: Journal of Applied Statistics Type: main |
| ResultId | 1 |