World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
35
Citations
5310
World Ranking
2773
National Ranking
55

Jorge Mateu publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Jorge Mateu sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 391 publications — 93rd percentile

93% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Jorge Mateu D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Jorge Mateu sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 35 D-Index — 25th percentile

25% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

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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