Estimation of a mixed effects model using a partially observed diffusion process
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| Autores: | , , , |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2019 |
| Descripción: | We consider a general mixed-effects model, where the variability of random effects of experimental individuals or units is incorporated through a stochastic differential equation. These models are useful for simultaneously analysing data from repeated measurements taken in discrete time and with errors. A Markov chain Monte Carlo algorithm was implemented to make the statistical inference a posteriori. A diagnostic analysis was carried out on the estimated parameters to detect if the model is suitable and show its convergence, in addition to the traces and posterior densities are shown. The methodology is illustrated using simulated data. |
| País: | Portal de Revistas UCR |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Lenguaje: | Español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/36223 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/36223 |
| Palabra clave: | mixed effects models stochastic differential equations Markov chains; Monte Carlo algorithms modelos de efectos mixtos ecuaciones diferenciales estocásticas algoritmos Monte Carlo por cadenas de Markov |