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Mathematics

D-Index
35
Citations
5310
World Ranking
2772
National Ranking
55

Overview

Jorge Mateu is affiliated with Jaume I University in Spain and has contributed extensively to the field of mathematics, with a particular focus on applied mathematics, economics and econometrics, epidemiology, modeling and simulation, and environmental engineering.

Their research spans several main topics, including:

  • Point processes and geometric inequalities
  • Spatial and panel data analysis
  • Data-driven disease surveillance
  • COVID-19 epidemiological studies
  • Morphological variations and asymmetry
  • Soil geostatistics and mapping
  • Bayesian methods and mixture models

Jorge Mateu's recent publications illustrate a range of interests related to spatial statistics, machine learning, and environmental risk assessment. Notable papers include:

  • "Nearest neighbour distance matching Leave-One-Out Cross-Validation for map validation," 2022, Methods in Ecology and Evolution
  • "Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks," 2020, IEEE Access
  • "A conditional machine learning classification approach for spatio-temporal risk assessment of crime data," 2023, Stochastic Environmental Research and Risk Assessment
  • "Inhomogeneous higher-order summary statistics for point processes on linear networks," 2020, Statistics and Computing
  • "Functional marked point processes: a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data," 2020, Test

Frequent co-authors working with Jorge Mateu include Jonatan A. González, Matthias Eckardt, Nicoletta D'Angelo, Giada Adelfio, and David Payares-Garcia.

The bulk of Jorge Mateu's research is disseminated through several key publication venues, reflecting the interdisciplinary nature of their work. These venues are:

  • arXiv (Cornell University)
  • Spatial Statistics
  • Stochastic Environmental Research and Risk Assessment
  • Test
  • Journal of the Royal Statistical Society Series A (Statistics in Society)

The researcher's work integrates methodologies from mathematics with practical applications in epidemiology, spatial data analysis, and environmental studies. Their engagement with Bayesian methods and mixture models also highlights an interest in combining statistical theory with data-driven approaches.

Best Publications

  • Statistics for spatial functional data: some recent contributions

    P. Delicado;R. Giraldo;R. Giraldo;C. Comas;J. Mateu

  • Ordinary kriging for function-valued spatial data

    R. Giraldo;R. Giraldo;P. Delicado;J. Mateu

  • Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach

    Moreno Bevilacqua;Carlo Gaetan;Jorge Mateu;Emilio Porcu

  • A COMPARISON BETWEEN PARAMETRIC AND NON-PARAMETRIC APPROACHES TO THE ANALYSIS OF REPLICATED SPATIAL POINT PATTERNS

    Peter J. Diggle;Jorge Mateu;Helen E. Clough

  • Spatial and Spatio-Temporal Geostatistical Modeling and Kriging

    José María Montero;Gema Fernández-Avilés;Jorge Mateu

  • Spatial and Spatio-Temporal Geostatistical Modeling and Kriging: Montero/Spatial and Spatio-Temporal Geostatistical Modeling and Kriging

    José-María Montero;Gema Fernández-Avilés;Jorge Mateu

  • Spatio-temporal point process statistics : a review

    Jonatan A. González;Francisco J. Rodríguez-Cortés;Ottmar Cronie;Jorge Mateu

  • A universal kriging approach for spatial functional data

    William Caballero;Ramón Giraldo;Jorge Mateu

  • Nonseparable stationary anisotropic space–time covariance functions

    E. Porcu;P. Gregori;J. Mateu

  • Case Studies in Spatial Point Process Modeling

    Adrian Baddeley;Pablo Gregori;Jorge Mateu;Radu Stoica

  • Continuous Time-Varying Kriging for Spatial Prediction of Functional Data: An Environmental Application

    R. Giraldo;P. Delicado;J. Mateu

  • Kriging with external drift for functional data for air quality monitoring

    Rosaria Ignaccolo;Jorge Mateu;Ramon Giraldo

  • The spatial pattern of a forest ecosystem

    J. Mateu;J.L. Usó;F. Montes

  • Hierarchical clustering of spatially correlated functional data

    Ramón Giraldo;Pedro Delicado;Jorge Mateu

  • Geostatistical methods to identify and map spatial variations of soil salinity

    P. Juan;J. Mateu;M.M. Jordan;J. Mataix-Solera

  • Hybrids of Gibbs Point Process Models and Their Implementation

    Adrian Baddeley;Rolf Turner;Jorge Mateu;Andrew Bevan

  • Discussion on the paper by Spiegelhalter, Sherlaw-Johnson, Bardsley, Blunt, Wood and Grigg

    Deborah Ashby;Sheila M. Bird;Ian Hunt;Robert Grant

  • Pinpointing spatio-temporal interactions in wildfire patterns

    P. Juan;J. Mateu;M. Saez

  • Allometric regression equations to determine aerial biomasses of Mediterranean shrubs

    J. L. Usó;J. Mateu;T. Karjalainen;P. Salvador

  • New classes of covariance and spectral density functions for spatio-temporal modelling

    E. Porcu;J. Mateu;F. Saura

  • Quasi-arithmetic means of covariance functions with potential applications to space-time data

    Emilio Porcu;Jorge Mateu;George Christakos

Frequent Co-Authors

Emilio Porcu
Emilio Porcu Khalifa University
Wenceslao González-Manteiga
Wenceslao González-Manteiga University of Santiago de Compostela
Adrian Baddeley
Adrian Baddeley Curtin University
Peter J. Diggle
Peter J. Diggle Lancaster University
Dietrich Stoyan
Dietrich Stoyan TU Bergakademie Freiberg
Rasmus Waagepetersen
Rasmus Waagepetersen Aalborg University
Jun Yu
Jun Yu Chinese University of Hong Kong
Andrew Gelman
Andrew Gelman Columbia University
Alan E. Gelfand
Alan E. Gelfand Duke University
Alfred Stein
Alfred Stein University of Twente

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