Dr Daniela Castro-Camilo

  • Senior Lecturer (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

Biography

My research revolves around environmental disaster-related statistics, creating methods and novel applications to learn about and predict disaster occurrences and their impacts. I do this through a combination of the theory and applications of multivariate and spatial extremes, spatial and spatio-temporal statistics, environmental statistics, causal inference and Bayesian inference.

Over the last few years, I have developed user-friendly methods that promote the need to adequately capture extreme observations within the usual statistical analysis centred around mean values. Most of my methods are available through the R-INLA package or GitHub.

I actively contribute to the statistical community, serving as the meeting secretary for the Environmental Section of the Royal Statistical Society, as an associated editor for the journal Environmetrics, as a member of the International Environmetrics Society outreach and liaison committee and as an Elected Member of the International Statistical Institute.

Research interests

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.
  • Climate extreme event attribution.
  • Point process modelling
  • 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 the EPSRC-funded project "GEOBEx: Geostatistical Binary Models for Extremes" which aims at developmenting a new framework for accurate estimation and prediction of binary spatio-temporal extremes.

Additional information

 

Research groups

Publications

List by: Type | Date

Jump to: 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017
Number of items: 15.

2023

Di Napoli, M., Tanyas, H., Castro-Camilo, D. , Calcaterra, D., Cevasco, A., Di Martire, D., Pepe, G., Brandolini, P. and Lombardo, L. (2023) On the estimation of landslide intensity, hazard and density via data-driven models. Natural Hazards, 119(3), pp. 1513-1530. (doi: 10.1007/s11069-023-06153-0)

Novellino, A., Ciurean, R., Bryce, E., Castro-Camilo, D. and Lombardo, L. (2023) Mitigating Landslides Impact in Scotland - MLIS. Summary Report. Documentation. National Centre for Resilience.

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 Jul 12 08:37:11 2024 BST.
Number of items: 15.

Articles

Di Napoli, M., Tanyas, H., Castro-Camilo, D. , Calcaterra, D., Cevasco, A., Di Martire, D., Pepe, G., Brandolini, P. and Lombardo, L. (2023) On the estimation of landslide intensity, hazard and density via data-driven models. Natural Hazards, 119(3), pp. 1513-1530. (doi: 10.1007/s11069-023-06153-0)

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)

Research Reports or Papers

Novellino, A., Ciurean, R., Bryce, E., Castro-Camilo, D. and Lombardo, L. (2023) Mitigating Landslides Impact in Scotland - MLIS. Summary Report. Documentation. National Centre for Resilience.

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 Jul 12 08:37:11 2024 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 - beta phase. Funded by the Office for Gas and Electricity Markets (UK government) in collaboration with Scottish Power, SIA Partners and the Met Office.

  3. GEOBEx: Geostatistical Binary Models for Extremes. Funded by EPSRC in collaboration with Carolina Euan (Lancaster University).

Supervision

Opportunities for PhD applicants

I'm always happy to discuss potential PhD projects with motivated applicants. I have a few potential PhD topics involving extremes, longitudinal data, disaster-related statistics, spatial statistics, and Bayesian inference, among others. But I'm also happy to discuss new ideas. If you're looking to do a PhD, please email me at daniela.castrocamilo@glasgow.ac.uk.

Current PhD students

  • Bryce, Erin
    Statistical landslide hazard modelling with a view towards medium to long term territorial planning
  • Cuba, Miriam
    Modelling Spatial Environmental Contamination Using Extreme Value Analysis
  • 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
  13. Rob Corner
  14. Cara McPhail

Teaching

Short courses and tutorials

Here, you can find a list of codes, tutorials and short courses.

UofG 2023/24 session

On study leave (sabbatical)

UofG 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.