Dr Daniela Castro-Camilo

  • Lecturer in Statistics (Statistics)

telephone: 01413304748
email: Daniela.CastroCamilo@glasgow.ac.uk

Room 315, Mathematics and Statistics Building, Glasgow G12 8QQ

Import to contacts

ORCID iDhttps://orcid.org/0000-0002-7536-4613

Research interests

My research focuses on the theory and applications of multivariate and spatial extremes, with a particular interest in environmental, geological, and ecological applications. During the last few years, my work has gravitated around the integrated nested Laplace approximation (INLA) method for Bayesian inference. Specifically, I have developed methods promoting the need to adequately capture extreme observations and collaborated in the implementation and improvement of extreme value models within INLA to help to bridge the gap between statistical theory and practice. I co-authored the book “Advanced spatial modelling with stochastic partial differential equations using R and INLA” (CRC Press, 2018).

Currently, my research interest is divided into the following topics:

  • Spatial and spatio-temporal extremes with applications to meteorological and environmental data (temperature, precipitation, wind speeds, soil pollutants).
  • Natural hazard modelling with a focus on landslide modelling using the SPDE-INLA approach.
  • I’m working on two grants at the moment. One is related to the forecast of faults in electricity networks, funded by OFGEM (office for gas and electricity markets). The second one is an NCR-funded project "Mitigating Landslides Impact in Scotland" which aims at combining a novel landslide susceptibility model with elements at risk to support emergency services and landslide hazard mitigation strategies in Scotland.
  • Geostatistics of binary extremes.
  • Climate extreme event attribution.

Research groups

Publications

List by: Type | Date

Jump to: 2022 | 2021 | 2020 | 2019 | 2018 | 2017
Number of items: 13.

2022

Castro-Camilo, D. , Huser, R. and Rue, H. (2022) Practical strategies for GEV-based regression models for extremes. Environmetrics, 33(6), e2742. (doi: 10.1002/env.2742)

Bryce, E., Lombardo, L., van Westen, C., Tanyas, H. and Castro-Camilo, D. (2022) Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica. Stochastic Environmental Research and Risk Assessment, 36(8), pp. 2071-2084. (doi: 10.1007/s00477-022-02239-6)

2021

Lombardo, L., Tanyas, H., Huser, R., Guzzetti, F. and Castro-Camilo, D. (2021) Landslide size matters: a new data-driven, spatial prototype. Engineering Geology, 293, 106288. (doi: 10.1016/j.enggeo.2021.106288)

Vandeskog, S. M., Martino, S. and Castro-Camilo, D. (2021) Modelling Block Maxima With the Blended Generalised Extreme Value Distribution. In: 22nd European Young Statisticians Meeting, 06-10 Sep 2021,

Castro-Camilo, D. , Mhalla, L. and Opitz, T. (2021) Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures. Extremes, 24(1), pp. 105-128. (doi: 10.1007/s10687-020-00394-z)

2020

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), pp. 1037-1054. (doi: 10.1080/01621459.2019.1647842)

2019

Amato, G., Eisank, C., Castro-Camilo, D. and Lombardo, L. (2019) Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment. Engineering Geology, 260, 105237. (doi: 10.1016/j.enggeo.2019.105237)

Castro-Camilo, D. , Huser, R. and 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), pp. 517-534. (doi: 10.1007/s13253-019-00369-z)

2018

Krainski, E. T., Gómez-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. Chapman & Hall/CRC: Boca Raton. ISBN 9781138369856 (doi: 10.1201/9780429031892)

Bakka, H. C., Castro-Camilo, D. , Franco-Villoria, M., Freni-Sterrantino, A., Huser, T. and Rue, H. (2018) Contributed discussion of "Using Stacking to Average Bayesian Predictive Distributions" by Yao et. al​. Bayesian Analysis, 13(3), pp. 982-985. (doi: 10.1214/17-BA1091)

Castro-Camilo, D. , de Carvalho, M. and Wadsworth, J. (2018) Time-varying extreme value dependence with application to leading European stock markets. Annals of Applied Statistics, 12(1), pp. 283-309. (doi: 10.1214/17-AOAS1089)

2017

Castro-Camilo, D. , Lombardo, L., Mai, P. M., Dou, J. and 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, pp. 145-156. (doi: 10.1016/j.envsoft.2017.08.003)

Castro-Camilo, D. and de Carvalho, M. (2017) Spectral density regression for bivariate extremes. Stochastic Environmental Research and Risk Assessment, 31(7), pp. 1603-1613. (doi: 10.1007/s00477-016-1257-z)

This list was generated on Fri Sep 22 05:23:04 2023 BST.
Jump to: Articles | Books | Conference Proceedings
Number of items: 13.

Articles

Castro-Camilo, D. , Huser, R. and Rue, H. (2022) Practical strategies for GEV-based regression models for extremes. Environmetrics, 33(6), e2742. (doi: 10.1002/env.2742)

Bryce, E., Lombardo, L., van Westen, C., Tanyas, H. and Castro-Camilo, D. (2022) Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica. Stochastic Environmental Research and Risk Assessment, 36(8), pp. 2071-2084. (doi: 10.1007/s00477-022-02239-6)

Lombardo, L., Tanyas, H., Huser, R., Guzzetti, F. and Castro-Camilo, D. (2021) Landslide size matters: a new data-driven, spatial prototype. Engineering Geology, 293, 106288. (doi: 10.1016/j.enggeo.2021.106288)

Castro-Camilo, D. , Mhalla, L. and Opitz, T. (2021) Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures. Extremes, 24(1), pp. 105-128. (doi: 10.1007/s10687-020-00394-z)

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), pp. 1037-1054. (doi: 10.1080/01621459.2019.1647842)

Amato, G., Eisank, C., Castro-Camilo, D. and Lombardo, L. (2019) Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment. Engineering Geology, 260, 105237. (doi: 10.1016/j.enggeo.2019.105237)

Castro-Camilo, D. , Huser, R. and 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), pp. 517-534. (doi: 10.1007/s13253-019-00369-z)

Bakka, H. C., Castro-Camilo, D. , Franco-Villoria, M., Freni-Sterrantino, A., Huser, T. and Rue, H. (2018) Contributed discussion of "Using Stacking to Average Bayesian Predictive Distributions" by Yao et. al​. Bayesian Analysis, 13(3), pp. 982-985. (doi: 10.1214/17-BA1091)

Castro-Camilo, D. , de Carvalho, M. and Wadsworth, J. (2018) Time-varying extreme value dependence with application to leading European stock markets. Annals of Applied Statistics, 12(1), pp. 283-309. (doi: 10.1214/17-AOAS1089)

Castro-Camilo, D. , Lombardo, L., Mai, P. M., Dou, J. and 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, pp. 145-156. (doi: 10.1016/j.envsoft.2017.08.003)

Castro-Camilo, D. and de Carvalho, M. (2017) Spectral density regression for bivariate extremes. Stochastic Environmental Research and Risk Assessment, 31(7), pp. 1603-1613. (doi: 10.1007/s00477-016-1257-z)

Books

Krainski, E. T., Gómez-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. Chapman & Hall/CRC: Boca Raton. ISBN 9781138369856 (doi: 10.1201/9780429031892)

Conference Proceedings

Vandeskog, S. M., Martino, S. and Castro-Camilo, D. (2021) Modelling Block Maxima With the Blended Generalised Extreme Value Distribution. In: 22nd European Young Statisticians Meeting, 06-10 Sep 2021,

This list was generated on Fri Sep 22 05:23:04 2023 BST.

Grants

 

  1. "Improving landslide hazard assessment in Scotland" funded by the Scottish Government through the National Centre for Resilience in collaboration with the British Geological Survey and the Department of Applied Earth Sciences, University of Twente. 

  2. "Predict4Resilience - alpha phase" funded by Office for Gas and Electricity Markets (UK government) in collaboration with Scottish Power, SIA Partners and the Met Office.

Supervision

Current PhD students

  • Bryce, Erin
    Statistical landslide hazard modelling with a view towards medium to long term territorial planning
  • Cuba, Miriam
    Understanding heavy-metal variation and extremes in the Glasgow Clyde River Basin using the G-BASE dataset
  • Hu, Chenglei
    Natural hazard risk estimation using Multivariate Extreme-Value Mixture Models (MEVMM)
  • Li, Mengran
    Climate extreme event attribution using sub-asymptotic models and counterfactual theory
  • Villejo, Stephen Jun
    A Bayesian Spatio-Temporal Model to Test for Stability of Risks for Spatially Misaligned Data

Honours projects supervision (2019-present)

  1. Caitlin Fox
  2. Tianhao Tan
  3. Mahi Siddika
  4. Paddy O'Hara
  5. Erin Bryce
  6. Louis Chislett
  7. Holly Moran
  8. Huinan Zhu
  9. Maria Tsiarkezou
  10. Samuele D'Avenia
  11. Sixiang Cheng
  12. Ula Wolanowska

Teaching

2022/2023 session

  1. STATS4047-STATS5022: Principles of Probability and Statistics. Moodle page (for enrolled students only).
  2. STATS5103: Introduction to Statistical programming in R and Python. Moodle page (for enrolled students only).

Additional information

Responsibilities

  1. I am the Deputy Exam Officer for Statistics. My main role is to manage and coordinate all Stats exam processes across all the levels, in collaboration with the office of teaching and the School Exams Officer.

  2. I am an academic advisor of studies for MSc students. My main role is to provide advice on course choices and offer pastoral support throughout their University career.