Development of multivariate dynamic models and their analysis by Bayesian mehtodology, using MCMC simulation methods. Incorporation of spatial dependencies into the temporal structure of the models. Design and implementation in R of algorithms for their analysis, estimation and prediction.
Application of automatic learning techniques for problems with prediction, classification and recognition of patterns or trends.
Longitudinal and survival data joint models with time or survival targets are studied, with special emphasis on dynamic predictive targets.
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.
ISO 17205 Standard. Development of ad hoc metrological tools, technical protocols, acceptance criteria, validation, quality assurance, control charts, uncertainty, including supporting software. Design of experiments, optimisation, simulation of future results.
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.
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.
Different financial data are analysed from a statistical perspective in order to design investment strategies.
