Theta-positive branching in varying environment

Branching processes in a varying environment encompass a wide range of stochastic demographic models, and their complete understanding in terms of limit behaviour poses a formidable research challenge. In this paper, we conduct a thorough investigation of such processes within a continuous-time framework, assuming that the reproduction law of individuals adheres to a specific parametric form for the probability generating function. Our six clear-cut limit theorems support the notion of recognizing five distinct asymptotical regimes for branching in varying environments: supercritical, asymptotically degenerate, critical, strictly subcritical, and loosely subcritical.

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Publication Details

Journal
Stochastics
Published
2025-11-12
DOI
https://doi.org/10.1080/17442508.2025.2585921
Primary Topic
Stochastic processes and statistical mechanics
Type
article
Field-Weighted Citation Impact
0.00
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article

Theta-positive branching in varying environment

Yerakhmet Zhumayev, Alexey Lindo, Serik Sagitov
Stochastics
Stochastic processes and statistical mechanics
article

Theta-positive branching in varying environment

Yerakhmet Zhumayev, Alexey Lindo, Serik Sagitov
article en

Abstract

Branching processes in a varying environment encompass a wide range of stochastic demographic models, and their complete understanding in terms of limit behaviour poses a formidable research challenge. In this paper, we conduct a thorough investigation of such processes within a continuous-time framework, assuming that the reproduction law of individuals adheres to a specific parametric form for the probability generating function. Our six clear-cut limit theorems support the notion of recognizing five distinct asymptotical regimes for branching in varying environments: supercritical, asymptotically degenerate, critical, strictly subcritical, and loosely subcritical.

Stochastics
L. N. Gumilyov Eurasian National University (KZ), Chalmers University of Technology (SE), University of Glasgow (GB), University of Gothenburg (SE)
Openalex Percentile: Top 59%
Stochastic processes and statistical mechanics
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