King, Ruth, McCrea, Rachel S. (2014) A generalised likelihood framework for partially observed capture–recapture–recovery models. Statistical Methodology, 17 . pp. 30-45. ISSN 1572-3127. (doi:10.1016/j.stamet.2013.07.004) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:37119)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
| Official URL: http://dx.doi.org/10.1016/j.stamet.2013.07.004 |
|
Abstract
We provide a closed form likelihood expression for multi-state capture–recapture–recovery data when the state of an individual may be only partially observed. The corresponding sufficient statistics are presented in addition to a matrix formulation which facilitates an efficient calculation of the likelihood. This likelihood framework provides a consistent and unified framework with many standard models applied to capture–recapture–recovery data as special cases.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1016/j.stamet.2013.07.004 |
| Uncontrolled keywords: | Capture–recapture–recovery data; Closed form likelihood; Multi-state; Partially observed states; Sufficient statistics |
| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics |
| Institutional Unit: | Schools > School of Engineering, Mathematics and Physics > Mathematical Sciences |
| Former Institutional Unit: |
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
|
| Depositing User: | Rachel McCrea |
| Date Deposited: | 02 Dec 2013 15:48 UTC |
| Last Modified: | 20 May 2025 11:35 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/37119 (The current URI for this page, for reference purposes) |
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):

https://orcid.org/0000-0002-3813-5328
Altmetric
Altmetric