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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

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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