Statistical inference for a stochastic generalized logistic differential equation

In this research we aim to estimate three parameters in a stochastic generalized logistic differential equation. We assume the intrinsic growth rate and shape parameters are constant but unknown. To estimate these two parameters, we use the maximum likelihood method and establish that the estimators...

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Veröffentlicht in:Communications in nonlinear science & numerical simulation Jg. 139; S. 108261
Hauptverfasser: Baltazar-Larios, Fernando, Delgado-Vences, Francisco, Diaz-Infante, Saul, Gomez, Eduardo Lince
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2024
Schlagworte:
Biological growth
Diffusion bridges
Expectation maximization algorithm
Maximum likelihood estimation
Stochastic generalized logistic differential equation
Maximum likelihood estimation
60H10
Stochastic generalized logistic differential equation
Expectation maximization algorithm
Biological growth
62F10
Diffusion bridges
60H35
ISSN:1007-5704
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Beschreibung
Zusammenfassung:In this research we aim to estimate three parameters in a stochastic generalized logistic differential equation. We assume the intrinsic growth rate and shape parameters are constant but unknown. To estimate these two parameters, we use the maximum likelihood method and establish that the estimators for these two parameters are strongly consistent. We estimate the diffusion parameter by using the quadratic variation processes. To test our results, we evaluate two data scenarios, complete and incomplete, with fixed values assigned to the three parameters. In the incomplete data scenario, we apply an Expectation Maximization algorithm. •MLE for three parameters in a stochastic generalized logistic differential equation.•The growth rate and shape are estimated using ML and they are shown to be consistent.•The ML estimator proposed exhibits a high level of efficiency and robustness.
ISSN:1007-5704
DOI:10.1016/j.cnsns.2024.108261