Generalized spatio-temporal models
An important problem in statistics is the study of spatio-temporal data taking into account the effect of explanatory variables such as latitude, longitude and time. In this paper, a new Bayesian approach for analyzing spatial longitudinal data is proposed. It takes into account linear time regressi...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2011 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:97724 |
| Acceso en línea: | https://ddd.uab.cat/record/97724 |
| Access Level: | acceso abierto |
| Palabra clave: | Bayesian Fisher scoring Mean-covariance modelling Antedependence Spatial statistics Spatial temporal models Spatial longitudinal data |
| Sumario: | An important problem in statistics is the study of spatio-temporal data taking into account the effect of explanatory variables such as latitude, longitude and time. In this paper, a new Bayesian approach for analyzing spatial longitudinal data is proposed. It takes into account linear time regression structures on the mean and linear regression structures on the variance-covariance matrix of normal observations. The spatial structure is included in the time regression parameters and also in the regression structure of the variance covariance matrix. Initially, we present a summary of the spatial models and the Bayesian methodology used to fit the models, as a extension of the longitudinal data analysis. Next, the general spatial temporal model is proposed. Finally, this proposal is used to study rainfall data. |
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