dc.contributor.author | Wahlstrom, J | |
dc.contributor.author | Skog, I | |
dc.contributor.author | Rosa, PSL | |
dc.contributor.author | Handel, P | |
dc.contributor.author | Nehorai, A | |
dc.date.accessioned | 2020-07-22T13:33:55Z | |
dc.date.issued | 2017-04-06 | |
dc.description.abstract | We study the maximum-likelihood estimator in a setting where the dependent variable is a random graph and covariates are available on a graph level. The model generalizes the well-known β-model for random graphs by replacing the constant model parameters with regression functions. Cramer-Rao bounds are derived for special cases of the undirected β-model, the directed β-model, and the covariate-based β-model. The corresponding maximum-likelihood estimators are compared with the bounds by means of simulations. Moreover, examples are given on how to use the presented maximum-likelihood estimators to test for directionality and significance. Finally, the applicability of the model is demonstrated using temporal social network data describing communication among healthcare workers. | en_GB |
dc.identifier.citation | Vol. 65, pp. 3234 - 3246 | en_GB |
dc.identifier.doi | 10.1109/tsp.2017.2691667 | |
dc.identifier.uri | http://hdl.handle.net/10871/122080 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses, in any current or future media, including
reprinting/republishing this material for advertising or promotional purposes, creating new
collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted
component of this work in other works | en_GB |
dc.subject | β-model | en_GB |
dc.subject | Cramer-Rao bounds | en_GB |
dc.subject | hypothesis testing | en_GB |
dc.subject | random graphs | en_GB |
dc.subject | dynamic social networks | en_GB |
dc.title | The β-model—maximum likelihood, Cramér–Rao bounds, and hypothesis testing | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-07-22T13:33:55Z | |
dc.identifier.issn | 1053-587X | |
dc.description | This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record | en_GB |
dc.identifier.journal | IEEE Transactions on Signal Processing | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2017-03-25 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2017-03-25 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-07-22T13:30:39Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-07-22T13:34:00Z | |
refterms.panel | B | en_GB |