Information properties of the nonparametric maximum likelihood estimators in branching processes with random migration
DOI:
https://doi.org/10.55630/j.biomath.2026.04.034Keywords:
branching processes, migration, nonparametric estimators, Fisher information matrix, confidence intervals, simulationsAbstract
We consider the nonparametric maximum likelihood estimation of single type discrete time branching processes with random migration. These processes occur naturally in situations where besides the random reproduction both emigration and immigration of individuals can be observed in the population. Our aim is to study the behaviour of the estimators by calculating the Fisher information matrix and in particular to present confidence intervals for the migration probabilities and for the offspring and immigration distribution, which to be further examined by simulations and computational results.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Tsvetomira Zlatkova, Vessela Stoimenova
This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal BIOMATH is an open access journal. All published articles are immediately available online and the respective DOI link activated. All articles can be accessed for free and no reader registration of any sort is required. No fees are charged to authors for article submission or processing. Online publications are funded through volunteer work, donations and grants.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
