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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.13841 |
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| _version_ | 1866914202743996416 |
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| author | Gutiérrez-Peña, Eduardo Pérez-Mendoza, Carlos Octavio Palacio, Alan Riva Siri-Jégousse, Arno |
| author_facet | Gutiérrez-Peña, Eduardo Pérez-Mendoza, Carlos Octavio Palacio, Alan Riva Siri-Jégousse, Arno |
| contents | In this article, we present a novel inference framework for estimating the parameters of Continuous-State Branching Processes (CSBPs). We do so by leveraging their subordinator representation. Our method reformulates the estimation problem by shifting the stochastic dynamics to the associated subordinator, enabling a parametric estimation procedure without requiring additional assumptions. This reformulation allows for efficient numerical recovery of the likelihood function via Laplace transform inversion, even in models where closed-form transition densities are unavailable. In addition to offering a flexible approach to parameter estimation, we propose a dynamic simulation framework that generates discrete-time trajectories of CSBPs using the same subordinator-based structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13841 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parameter Estimation for Partially Observed Stable Continuous-State Branching Processes Gutiérrez-Peña, Eduardo Pérez-Mendoza, Carlos Octavio Palacio, Alan Riva Siri-Jégousse, Arno Methodology In this article, we present a novel inference framework for estimating the parameters of Continuous-State Branching Processes (CSBPs). We do so by leveraging their subordinator representation. Our method reformulates the estimation problem by shifting the stochastic dynamics to the associated subordinator, enabling a parametric estimation procedure without requiring additional assumptions. This reformulation allows for efficient numerical recovery of the likelihood function via Laplace transform inversion, even in models where closed-form transition densities are unavailable. In addition to offering a flexible approach to parameter estimation, we propose a dynamic simulation framework that generates discrete-time trajectories of CSBPs using the same subordinator-based structure. |
| title | Parameter Estimation for Partially Observed Stable Continuous-State Branching Processes |
| topic | Methodology |
| url | https://arxiv.org/abs/2512.13841 |