A Bayesian non-stationary heteroskedastic time series model for multivariate critical care data.
Stat Med
; 43(20): 3958-3974, 2024 Sep 10.
Article
en En
| MEDLINE
| ID: mdl-38956865
ABSTRACT
We propose a multivariate GARCH model for non-stationary health time series by modifying the observation-level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state-space models. We follow the Bayesian paradigm to perform the inference procedure. In particular, we use Markov chain Monte Carlo methods to obtain samples from the resultant posterior distribution. We use the forward filtering backward sampling algorithm to efficiently obtain samples from the posterior distribution of the latent state. The proposed model also handles missing data in a fully Bayesian fashion. We validate our model on synthetic data and analyze a data set obtained from an intensive care unit in a Montreal hospital and the MIMIC dataset. We further show that our proposed models offer better performance, in terms of WAIC than standard state space models. The proposed model provides a new way to model multivariate heteroskedastic non-stationary time series data. Model comparison can then be easily performed using the WAIC.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Método de Montecarlo
/
Cadenas de Markov
/
Modelos Estadísticos
/
Teorema de Bayes
/
Cuidados Críticos
/
Unidades de Cuidados Intensivos
Límite:
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Stat Med
Año:
2024
Tipo del documento:
Article
País de afiliación:
Canadá
Pais de publicación:
Reino Unido