Research Interests

Alan Gelfand told me once that the best way to describe what we do is to say that we are stochastic modellers. That is what I try to do, to model complex real situations in the presence of uncertainty. To do so, the way I liked the most is the Bayesian approach. Being from Valencia, this can be considered as kind of natural.

In line with this, I have been involved in the analysis of waiting lists for transplants, efficiency of banks, early detection of epidemics, environmental performance, and so on. Since 2010, species distribution models has been my main focus, with works related to find the statistical tools and the best models that could help us to describe the spatial distribution of plant diseases, veterinary diseases, and fish species.

Research indicators

  1. Publons
  2. Research Gate
  3. ORCID
  4. Google Scholar
  5. Scopus

10 Selected Publications

In reverse chronological order:

  1. B. Sarzo, D. Conesa and R. King (2020). Cormack-Jolly-Seber models: time and age perspectives. Stochastic Environmental Research and Risk Assessment, 34: 1683–1698.
  2. X. Barber, D. Conesa, A. López-Quílez and J. Morales (2019). Multivariate Bioclimatic indices modelling: A coregionalised approach. Journal of Agricultural, Biological and Environmental Statistics, 24(2), 225–244.
  3. J. Martínez-Minaya, M. Cameletti, D. Conesa and M.G. Pennino (2018). Species distribution modeling: a statistical review with focus in spatio-temporal issues. Stochastic Environmental Research and Risk Assessment, 32, 3227–3244.
  4. I. Paradinas, D. Conesa, A. López-Quílez and J. M. Bellido (2017). Spatio-Temporal model structures with shared components for semi-continuous species distribution modelling. Spatial Statistics, 22, 434–450.
  5. R. Gómez-Calvet, D. Conesa, A. Gómez-Calvet and Emili Tortosa-Ausina (2016). On the dynamics of eco-efficiency performance in the European Union. Computers and Operations Research, 66, 336–350.
  6. D. Conesa, M.A. Martínez-Beneito, R. Amorós and A. López-Quílez (2015). Bayesian Hierarchical Poisson Models with a hidden Markov structure for the detection of influenza epidemic outbreaks. Statistical Methods in Medical Research, 24(2): 206–223.
  7. F. Muñoz, M. G. Pennino, D. Conesa, A. López-Quílez and J. M. Bellido (2013). Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models. Stochastic Environmental Research and Risk Assessment, 27: 1171–1180.
  8. E. Tortosa, E. Grifell, C. Armero and D. Conesa (2008). Sensitivity analysis of efficiency and Malmquist productivity indices: an application to Spanish saving banks. European Journal of Operational Research, 184(3), 1062–1084.
  9. M. A. Martínez-Beneito, D. Conesa, A. López-Quílez and A. López-Maside (2008). Bayesian Markov switching models for the early detection of influenza epidemics. Statistics in Medicine, 27(22), 4455–4468.
  10. C. Armero and D. Conesa (2000). Prediction in Markovian bulk arrival queues. Queueing Systems, 34, 327–350.