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Francisco Cribari-Neto

Francisco Cribari-Neto

D-Index & Metrics

Mathematics

D-Index
31
Citations
10358
World Ranking
3264
National Ranking
34

Francisco Cribari-Neto 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 Francisco Cribari-Neto 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: 82 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: 155 publications — 41st percentile

41% of scientists in this discipline score the same or lower.

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

Francisco Cribari-Neto 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 Francisco Cribari-Neto 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: 137 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: 31 D-Index — 9th percentile

9% of scientists in this discipline score the same or lower.

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

Overview

Francisco Cribari-Neto is affiliated with the Federal University of Pernambuco in Brazil. Their research primarily focuses on mathematics, with a significant concentration in statistics and probability as well as applications spanning economics, econometrics, management science, operations research, global and planetary change, and sociology and political science.

The main topics explored in their work include advanced statistical methods and models, statistical methods and Bayesian inference, statistical distribution estimation and applications, water resources management and optimization, monetary policy and economic impact, forecasting techniques and applications, and hydrological forecasting using artificial intelligence.

Recent publications authored or co-authored by Francisco Cribari-Neto include:

  • Beta autoregressive moving average model selection with application to modeling and forecasting stored hydroelectric energy, 2021, International Journal of Forecasting
  • Generalized βARMA model for double bounded time series forecasting, 2023, International Journal of Forecasting
  • Bias and variance residuals for machine learning nonlinear simplex regressions, 2021, Expert Systems with Applications
  • Improved testing inferences for beta regressions with parametric mean link function, 2020, AStA Advances in Statistical Analysis
  • A beta regression analysis of COVID-19 mortality in Brazil, 2023, Infectious Disease Modelling

The publication venues where Francisco Cribari-Neto has frequently contributed include:

  • International Journal of Forecasting
  • Statistica Neerlandica
  • PLoS ONE
  • River Research and Applications
  • Expert Systems with Applications

Frequent co-authors collaborating with Cribari-Neto are:

  • Fábio M. Bayer
  • Vinícius Teodoro Scher
  • Patrícia L. Espinheira
  • José Jairo Santana-e-Silva
  • Klaus L. P. Vasconcellos

Best Publications

  • Beta Regression for Modelling Rates and Proportions

    Sílvia Lopes de Paula Ferrari;Francisco Cribari-Neto

  • Beta Regression in R

    Francisco Cribari-Neto;Achim Zeileis

  • Asymptotic inference under heteroskedasticity of unknown form

    Francisco Cribari-Neto

  • On beta regression residuals

    Patrícia L. Espinheira;Silvia L.P. Ferrari;Francisco Cribari-Neto

  • A generalization of the exponential-Poisson distribution

    Wagner Barreto-Souza;Francisco Cribari-Neto

  • Improved statistical inference for the two-parameter Birnbaum-Saunders distribution

    Artur J. Lemonte;Francisco Cribari-Neto;Klaus L. P. Vasconcellos

  • Bootstrap methods for heteroskedastic regression models: evidence on estimation and testing

    F. Cribari-Neto;S. G. Zarkos

  • Influence diagnostics in beta regression

    Patrícia L. Espinheira;Silvia L. P. Ferrari;Francisco Cribari-Neto

  • Improved point and interval estimation for a beta regression model

    Raydonal Ospina;Francisco Cribari-Neto;Klaus L. P. Vasconcellos

  • On bartlett and bartlett-type corrections francisco cribari-neto

    Francisco Cribari-Neto;Gauss M. Cordeiro

  • Beta autoregressive moving average models

    Andréa V. Rocha;Francisco Cribari-Neto

  • On Bartlett and Bartlett-Type Corrections

    F. Cribari-Neto;G.M. Cordeiro

  • R: yet another econometric programming environment

    Francisco Cribari-Neto;Spyros G. Zarkos

  • Explaining DEA Technical Efficiency Scores in an Outlier Corrected Environment: The Case of Public Services in Brazilian Municipalities

    Maria da Conceição Sampaio de Sousa;Francisco Cribari-Neto;Borko D. Stosic

  • An Introduction to Bartlett Correction and Bias Reduction

    Gauss M. Cordeiro;Francisco Cribari-Neto

  • Inference Under Heteroskedasticity and Leveraged Data

    Francisco Cribari-Neto;Tatiene C. Souza;Klaus L. P. Vasconcellos

  • A new heteroskedasticity-consistent covariance matrix estimator for the linear regression model

    Francisco Cribari-Neto;Wilton Bernardino da Silva

  • Nearly Unbiased Maximum Likelihood Estimation for the Beta Distribution

    Francisco Cribari-Neto;Klaus L. P. Vasconcellos

  • Improved heteroscedasticity‐consistent covariance matrix estimators

    Francisco Cribari‐Neto;Silvia L. P. Ferrari;Gauss M. Cordeiro

  • Robust estimation in long-memory processes under additive outliers

    Fabio Fajardo Molinares;Valdério Anselmo Reisen;Francisco Cribari-Neto

Frequent Co-Authors

Silvia Ferrari
Silvia Ferrari Cornell University
Gauss M. Cordeiro
Gauss M. Cordeiro Federal University of Pernambuco
Achim Zeileis
Achim Zeileis University of Innsbruck

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