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Main Authors: Catanzaro, Simone, Di Nardo, Elvira
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2604.18253
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author Catanzaro, Simone
Di Nardo, Elvira
author_facet Catanzaro, Simone
Di Nardo, Elvira
contents The first passage time problem is considered for stochastic logistic growth model with constant harvesting and multiplicative environmental noise. Explicit expressions for the moments and cumulants of both upcrossing and downcrossing FPTs in the presence of constant thresholds are obtained through a power-series expansion of the Laplace transform. Then a closed-form representation of the FPT density is recovered via an orthogonal Laguerre--Gamma expansion . This representation is used to numerically evaluate FPT densities, with the truncation order controlling the trade-off between accuracy and stability. Numerical experiments based on Monte Carlo simulations confirm the high accuracy of the method in regimes of moderate dispersion and highlight its limitations when higher-order moments grow rapidly. Application to fisheries management models shows that the method remains effective even for large-scale population. Finally, the approximated density is satisfactory used to estimate some parameters of the model.
format Preprint
id arxiv_https___arxiv_org_abs_2604_18253
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Gamma-Based Expansion for the First-Passage Time Distribution of Stochastic Logistic Models with Harvesting
Catanzaro, Simone
Di Nardo, Elvira
Statistics Theory
Probability
Computation
The first passage time problem is considered for stochastic logistic growth model with constant harvesting and multiplicative environmental noise. Explicit expressions for the moments and cumulants of both upcrossing and downcrossing FPTs in the presence of constant thresholds are obtained through a power-series expansion of the Laplace transform. Then a closed-form representation of the FPT density is recovered via an orthogonal Laguerre--Gamma expansion . This representation is used to numerically evaluate FPT densities, with the truncation order controlling the trade-off between accuracy and stability. Numerical experiments based on Monte Carlo simulations confirm the high accuracy of the method in regimes of moderate dispersion and highlight its limitations when higher-order moments grow rapidly. Application to fisheries management models shows that the method remains effective even for large-scale population. Finally, the approximated density is satisfactory used to estimate some parameters of the model.
title Gamma-Based Expansion for the First-Passage Time Distribution of Stochastic Logistic Models with Harvesting
topic Statistics Theory
Probability
Computation
url https://arxiv.org/abs/2604.18253