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...

Full description

Saved in:

Bibliographic Details
Published in:	Communications in nonlinear science & numerical simulation Vol. 139; p. 108261
Main Authors:	Baltazar-Larios, Fernando, Delgado-Vences, Francisco, Diaz-Infante, Saul, Gomez, Eduardo Lince
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2024
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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