- Compositional data modelling.Development of models with compositional data. In environments such as biology, economics or geology, it is common to work with data vectors whose components reflect the relative contribution of different parts in relation to a total, obtaining compositional samples. Work will be done on the progress of statistical modelling of compositional data, its application and its mathematical foundations.
- Disease mapping.Development of Bayesian hierarchical models for the study of the geographical variability of diseases and their temporal evolution with the aim of aiding decision-making and the development of surveillance programmes.
- Bayesian Multivariate Time Series Analysis.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.
- Investment portfolio selection models.Development of portfolio selection models, in which uncertainty about performance is described by possibility and credibility distributions. Multi-objective analysis of the selection problem, seeking approximations to the Pareto front using evolutionary algorithms.
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