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Description

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:

  1. Hierarchical models in studies with correlated data.
  2. Model selection.
  3. 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.

Goals CT
  • To produce methodological research in Bayesian Statistics of scientific quality nationally and internationally recognised.
  • To produce methodological research in Bayesian Statistics that might be useful for our society.
Research lines
  • 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.

  • 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.

  • 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.

Management
  • ARMERO CERVERA, CARMEN
  • PDI-Catedratic/a d'Universitat
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Members
  • CONESA GUILLEN, DAVID VALENTIN
  • PDI-Catedratic/a d'Universitat
  • Director/a de Departament
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  • LOPEZ QUILEZ, ANTONIO MANUEL
  • PDI-Catedratic/a d'Universitat
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Non-UV research staff

Collaborators

  • Xavier Barber Vallés - UMH-Alicante
  • Stefano Cabras - U3CM-Madrid
  • María Eugenia Castellanos Nueda - URJC-Madrid
  • Gonzalo García-Donato Lairón - UCLM-Castilla-La Mancha
  • Virgilio Gómez Rubio - UCLM-Castilla-La Mancha
  • Paradinas Aranjuelo Iosu - UVEG-Valencia
  • Joaquín Martínez Minaya - IVIA-Valencia
  • Facundo Martín Muñoz Viera - FRA-INRA
Scientific production by UV researcher
  • LOPEZ QUILEZ, ANTONIO MANUEL
    PDI-Catedratic/a d'Universitat
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Associated structure
Statistics and Operational Research
Contact group details
Valencia Bayesian Research Group (VABAR)

Burjassot/Paterna Campus

C/ Dr. Moliner, 50

46100 Burjassot (Valencia)

+34 963 544 309

Geolocation

vabar.es

carmen.armero@uv.es

Contact people
  • ARMERO CERVERA, CARMEN
  • PDI-Catedratic/a d'Universitat
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