GIUV2016-271
The objective of our group is the methodological and applied research of Bayesian Statistics, especially in scenarios of epidemiological and environmental type. Our work is fundamentally based on three axes:
Hierarchical models in studies with correlated data.
Model selection.
Computational simulation models. All of them are somewhat mixed in nature, methodological and applied, and in the compatibility and interrelation of many of their knowledge and objectives. The first thematic block is the most extensive and is dedicated to research on models with correlated data associated with structures of a spatial-temporal, longitudinal, survival or non-survival type, and of blood kinship. Methodological research in disease mapping has a long tradition in our team, currently with unbalanced multivariate and spatial-temporal objectives. This block also contains new research proposals dedicated to joint models with longitudinal and survival data, methodology on species distribution, spatial-temporal surveillance of diseases and regression methods for scattered genetic data from genetically isolated populations with known family trees, which will undoubtedly lead us to Big Data. In the...The objective of our group is the methodological and applied research of Bayesian Statistics, especially in scenarios of epidemiological and environmental type. Our work is fundamentally based on three axes:
Hierarchical models in studies with correlated data.
Model selection.
Computational simulation models. All of them are somewhat mixed in nature, methodological and applied, and in the compatibility and interrelation of many of their knowledge and objectives. The first thematic block is the most extensive and is dedicated to research on models with correlated data associated with structures of a spatial-temporal, longitudinal, survival or non-survival type, and of blood kinship. Methodological research in disease mapping has a long tradition in our team, currently with unbalanced multivariate and spatial-temporal objectives. This block also contains new research proposals dedicated to joint models with longitudinal and survival data, methodology on species distribution, spatial-temporal surveillance of diseases and regression methods for scattered genetic data from genetically isolated populations with known family trees, which will undoubtedly lead us to Big Data. In the area dedicated to the selection of models, marginal, conditioned, and combined measures are studied to quantify the contribution of a potential set of covariates in the explanation of a response of interest, and two new lines have been initiated, with a more applied orientation, which link the subject of variable selection with longitudinal and survival models and related data through structures of consanguinity. Finally, in the block of computational models, the group continues with a line of action dedicated to the calibration of multivariate computational models and the implementation of the results obtained in a computer application and in a new application dedicated to uncertainty modelling in compartmental models.
[Read more][Hide]
[Read more][Hide]
- Producir una investigacion metodologica en Estadistica Bayesiana de calidad cientifica que sea reconocida nacional e internacionalmente
- Producir una investigacion metodologica en Estadistica Bayesiana que pueda ser util para nuestra sociedad
- Longitudinal and survival data joint models.Longitudinal and survival data joint models with time or survival targets are studied, with special emphasis on dynamic predictive targets.
- Spatio-temporal disease surveillance.Development of statistical methodologies for the rapid and reliable detection of influenza epidemics. We approach the inference and prediction of these models from the Bayesian paradigm, which allows the implementation of complex models with spatial, temporal and hierarchical structures.
- Species distribution models.Development of models to predict the spatial and spatial-temporal distribution of species. The incorporation of uncertainty in covariates, the problems generated by missing values, the effect of preferential sampling and the handling of large volumes of data are addressed.
- Model selection.Our group addresses the problem of model selection from a Bayesian target point of view. We particularly work on the study of criteria that allow us to establish optimal prior distributions in order to carry out an effective selection and modeling.
| Name | Nature of participation | Entity | Description |
|---|---|---|---|
| MARIA CARMEN ARMERO CERVERA | Director | Universitat de València | |
| Research team | |||
| ANTONIO MANUEL LOPEZ QUILEZ | Member | Universitat de València | |
| DAVID VALENTIN CONESA GUILLEN | Member | Universitat de València | |
| ANABEL FORTE DELTELL | Member | Universitat de València | |
| MIGUEL ÁNGEL MARTINEZ BENEITO | Member | Universitat de València | |
| VIRGILIO GOMEZ RUBIO | Collaborator | Universidad de Castilla la Mancha | pre-tenured lecturer |
| FACUNDO MARTIN MUÑOZ VIERA | Collaborator | Institut National de la Recherche Agronomique (Francia) | postdoctoral researcher |
| HECTOR PERPIÑAN FABUEL | Collaborator | Universitat de València - Estudi General | UVEG PhD student |
| XAVIER BARBER VALLES | Collaborator | Universidad Miguel Hernández de Elche | pre-tenured lecturer |
| STEFANO CABRAS | Collaborator | Universidad Carlos III Madrid | postdoctoral researcher |
| MARIA EUGENIA CASTELLANOS NUEDA | Collaborator | Universidad Rey Juan Carlos | tenured university professor |
| GONZALO GARCIA-DONATO LAIRON | Collaborator | Universidad de Castilla la Mancha | tenured university professor |
- -
- Statistics and Operational Research
- Cadenas de Markov ocultas; Censura y truncamiento; Datos ausentes; Efectos aleatorios;Métodos de captura y recaptura; Predicción dinámica.
- Autorregresivo; Detección de epidemias; Espacio-temporal; Estadística bayesiana; Gripe; Modelos ocultos de Markov; Vigilancia epidemiológica.
- Incertidumbre en las covariables; Modelos predictivos; Modelos espaciotemporales; Muestreo preferencial; Valores ausentes;
- Distribuciones objetivas; Selección de modelos; Selección de variables
