On parameter estimation for stochastic logistic growth laws through the maximum likelihood procedure
A three alternative stochastic logistic growth models (exponential, Verhulst, Gompertz) are used for modelling of growth processes. The aim of this paper is to develop stochastic growth curves for all three stochastic growth models. Estimates of parameters of the stochastic growth models are perfor...
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| Format: | Article |
| Language: | English |
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Vilnius University Press
2004-12-01
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| Series: | Lietuvos Matematikos Rinkinys |
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| Online Access: | https://www.journals.vu.lt/LMR/article/view/32259 |
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| _version_ | 1832593177814499328 |
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| author | Petras Rupšys |
| author_facet | Petras Rupšys |
| author_sort | Petras Rupšys |
| collection | DOAJ |
| description |
A three alternative stochastic logistic growth models (exponential, Verhulst, Gompertz) are used for modelling of growth processes. The aim of this paper is to develop stochastic growth curves for all three stochastic growth models. Estimates of parameters of the stochastic growth models are performed by the maximum likelihood procedure with the local linearization method. The Milshtein discrete time approximation for the solutions of stochastic differential equations is applied.
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| format | Article |
| id | doaj-art-2cc7096d1a564b5086d0ee926fdc2119 |
| institution | Kabale University |
| issn | 0132-2818 2335-898X |
| language | English |
| publishDate | 2004-12-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Lietuvos Matematikos Rinkinys |
| spelling | doaj-art-2cc7096d1a564b5086d0ee926fdc21192025-01-20T18:16:19ZengVilnius University PressLietuvos Matematikos Rinkinys0132-28182335-898X2004-12-0144spec.10.15388/LMR.2004.32259On parameter estimation for stochastic logistic growth laws through the maximum likelihood procedurePetras Rupšys0Lithuanian Academy of Agriculture A three alternative stochastic logistic growth models (exponential, Verhulst, Gompertz) are used for modelling of growth processes. The aim of this paper is to develop stochastic growth curves for all three stochastic growth models. Estimates of parameters of the stochastic growth models are performed by the maximum likelihood procedure with the local linearization method. The Milshtein discrete time approximation for the solutions of stochastic differential equations is applied. https://www.journals.vu.lt/LMR/article/view/32259stochastic differential equationapproximationlocal linearizationsimulationstand |
| spellingShingle | Petras Rupšys On parameter estimation for stochastic logistic growth laws through the maximum likelihood procedure Lietuvos Matematikos Rinkinys stochastic differential equation approximation local linearization simulation stand |
| title | On parameter estimation for stochastic logistic growth laws through the maximum likelihood procedure |
| title_full | On parameter estimation for stochastic logistic growth laws through the maximum likelihood procedure |
| title_fullStr | On parameter estimation for stochastic logistic growth laws through the maximum likelihood procedure |
| title_full_unstemmed | On parameter estimation for stochastic logistic growth laws through the maximum likelihood procedure |
| title_short | On parameter estimation for stochastic logistic growth laws through the maximum likelihood procedure |
| title_sort | on parameter estimation for stochastic logistic growth laws through the maximum likelihood procedure |
| topic | stochastic differential equation approximation local linearization simulation stand |
| url | https://www.journals.vu.lt/LMR/article/view/32259 |
| work_keys_str_mv | AT petrasrupsys onparameterestimationforstochasticlogisticgrowthlawsthroughthemaximumlikelihoodprocedure |