Λεπτομέρειες βιβλιογραφικής εγγραφής
| 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 |