1. Krainski, E. T., Gomez-Rubio, V., Bakka, H., Lenzi, A., Castro-Camilo, D., Simpson, D., Lindgren, F., and Rue, H. (2018). Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA. CRC press.

Published and accepted papers

  1. Lombardo, L., Tanyas, H., Huser, R., Guzzetti, F., & Castro-Camilo, D. (2021). Landslide size matters: A new data-driven, spatial prototypeEngineering Geology, 106288.
  2. Castro-Camilo, D., Mhalla, L., & Opitz, T. (2021). Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperaturesExtremes24(1), 105-128.
  3. Castro-Camilo, D. and Huser, R. (2020). Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes. Journal of the American Statistical Association, 115:531, 1037-1054. ‼️🆓 Get a free copy ➡
  4. Castro-Camilo, D., Huser, R. & Rue, H. (2019). A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting. Journal of Agricultural, Biological and Environmental Statistics, 24(3), 517-534.
  5. Lombardo, L., Amato, G., Eisank, C., Castro-Camilo, D. (2019). Accounting for covariate distributions in Slope-Unit-based landslide susceptibility models. A case study in the Alpine environment. Engineering Geology. DOI: ‼️🆓 Get a free copy until Sept. 27 ➡
  6. Castro-Camilo, D., de Carvalho, M. and Wadsworth, J. L. (2018). Time-varying extreme value dependence with application to leading European stock markets. Annals of Applied Statistics, 12(1), 283-309.
  7. Castro-Camilo, D. and de Carvalho, M. (2017). Spectral density regression for bivariate extremes. Stochastic Environmental Research and Risk Assessment, 31(7), 1603-1613.
  8. Castro-Camilo, D., Lombardo, L., Dou, J., Mai, M., Huser, R. (2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model. Environmental Modelling and Software, 97, 145-156.

Submitted papers

  1. Castro-Camilo, D., Huser, R., & Rue, H. (2021). Practical strategies for GEV-based regression models for extremes.
  2. Vandeskog, S. M., Martino, S., Castro-Camilo, D., & Rue, H. (2021). Modelling short-term precipitation extremes with the blended generalised extreme value distribution.

Published discussions

  1. Bakka, H., Castro-Camilo, D., Franco-Villoria, M., Freni-Sterrantino, A., Huser, R., Opitz, T., and Rue, H. (2018), Discussion to the paper: “Using Stacking to Average Bayesian Predictive Distributions.” by Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. Bayesian Analysis, advance publication, 16 January 2018. doi:10.1214/17-BA1091. To appear.
  2. Castro, D. A. and Porcu, E. (2015). Discussion to the paper: Sequential quasi-monte-carlo sampling.  Journal of the Royal Statistical Society, Series B.