Spatio-temporal prediction of missing temperature with stochastic Poisson equations. The LC2019 team winning entry for the EVA 2019 data competition

DOI10.1007/s10687-020-00397-wzbMath1466.62423OpenAlexW3101524414MaRDI QIDQ2028576

Zishun Liu, Dan Cheng

Publication date: 1 June 2021

Published in: Extremes (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10687-020-00397-w

zbMATH Keywords

predictionPoisson equationspatio-temporal datatemperature datadata competitionEVA 2019 conference

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Inference from stochastic processes and prediction (62M20) Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) PDEs in connection with statistics (35Q62)

Uses Software

• ElemStatLearn

Cites Work

• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• A hierarchical max-stable spatial model for extreme precipitation
• Statistical inference using extreme order statistics
• Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes
• Numerical Methods for Elliptic and Parabolic Partial Differential Equations
• Statistical Inference for Max-Stable Processes in Space and Time
• Space–Time Modelling of Extreme Events
• Hierarchical Space-Time Modeling of Asymptotically Independent Exceedances With an Application to Precipitation Data
• Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
• Generalized Additive Modelling of Sample Extremes
• Statistical modeling of spatial extremes
• Unnamed Item

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