About Daniela Castro-Camilo Daniela Castro-Camilo Postdoctoral Research Fellow, Statistics Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational statistics INLA Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015 Articles Related News May 2022 New paper accepted in Environmetrics - 2022 1 min read · Thu, May 19 2022 Spotlight News Statistics of extremes New paper accepted: Castro-Camilo, D., Huser, R., and Rue, H. (2022+), Practical strategies for GEV-based regression models for extremes, Environmetrics, to appear [ PDF preprint] December 2019 A second wind for turbine design 1 min read · Sun, Dec 8 2019 News renewable energy wind energy statistics Better prediction of extreme winds at sparsely observed locations could help optimize the design of wind farms. September 2019 Expanding the scale of dangerous weather prediction 1 min read · Sun, Sep 29 2019 News Environmental Statistics A more accurate and efficient method of capturing the local factors that lead to extreme rainfall enables better flood prediction across larger regions. August 2019 Congratulations to Daniela for her new position as Lecturer in Statistics at the University of Glasgow 1 min read · Mon, Aug 19 2019 News Spotlight July 2019 Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes 1 min read · Thu, Jul 4 2019 News Spatial extremes New accepted paper: Castro Camilo, D., and Huser, R. (2019+), Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes, Journal of the American Statistical Association, to appear. June 2019 New paper accepted in JABES 1 min read · Tue, Jun 25 2019 Spotlight News extreme weather spatio-temporal statistics Statistics of extremes New accepted paper: 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, to appear [ PDF preprint] February 2019 Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA book 1 min read · Tue, Feb 19 2019 News INLA statistics xstat research highlights Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real July 2016 Prof. Miguel de Carvalho and Rodrigo Rubio (PUC, Chile) visit extSTAT 1 min read · Fri, Jul 15 2016 News Statistics of extremes Prof. Miguel Carvalho holds a research and teaching positions at PUC Chile, EPFL, Banco de Portugal, and UNL. He is an applied mathematical statistician with a variety of research interests including, inter alia, statistical inferences for small-probability events, geometrical statistics, methods for data visualization and graphical learning, econometrics, and medical diagnostic assessment.
New paper accepted in Environmetrics - 2022 1 min read · Thu, May 19 2022 Spotlight News Statistics of extremes New paper accepted: Castro-Camilo, D., Huser, R., and Rue, H. (2022+), Practical strategies for GEV-based regression models for extremes, Environmetrics, to appear [ PDF preprint]
A second wind for turbine design 1 min read · Sun, Dec 8 2019 News renewable energy wind energy statistics Better prediction of extreme winds at sparsely observed locations could help optimize the design of wind farms.
Expanding the scale of dangerous weather prediction 1 min read · Sun, Sep 29 2019 News Environmental Statistics A more accurate and efficient method of capturing the local factors that lead to extreme rainfall enables better flood prediction across larger regions.
Congratulations to Daniela for her new position as Lecturer in Statistics at the University of Glasgow 1 min read · Mon, Aug 19 2019 News Spotlight
Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes 1 min read · Thu, Jul 4 2019 News Spatial extremes New accepted paper: Castro Camilo, D., and Huser, R. (2019+), Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes, Journal of the American Statistical Association, to appear.
New paper accepted in JABES 1 min read · Tue, Jun 25 2019 Spotlight News extreme weather spatio-temporal statistics Statistics of extremes New accepted paper: 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, to appear [ PDF preprint]
Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA book 1 min read · Tue, Feb 19 2019 News INLA statistics xstat research highlights Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real
Prof. Miguel de Carvalho and Rodrigo Rubio (PUC, Chile) visit extSTAT 1 min read · Fri, Jul 15 2016 News Statistics of extremes Prof. Miguel Carvalho holds a research and teaching positions at PUC Chile, EPFL, Banco de Portugal, and UNL. He is an applied mathematical statistician with a variety of research interests including, inter alia, statistical inferences for small-probability events, geometrical statistics, methods for data visualization and graphical learning, econometrics, and medical diagnostic assessment.
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