World's Best Scientists 2026 revealed!

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Mathematics

D-Index
39
Citations
9748
World Ranking
2136
National Ranking
906

Overview

Silvia Ferrari is affiliated with Cornell University in the United States and focuses primarily on research in mathematics. Within this broad field, their work concentrates on statistics and probability, with significant contributions across related areas such as statistics, probability and uncertainty, and aspects of clinical psychology.

The main topics explored in Ferrari's research include advanced statistical methods and models, statistical distribution estimation and applications, and statistical methods and inference. Additional focal points are advanced statistical process monitoring, eating disorders and behaviors, obsessive-compulsive spectrum disorders, as well as statistical methods and Bayesian inference.

Frequent coauthors collaborating with Ferrari include Francisco F. Queiroz, Felipe Q. da Luz, Mohammed Mohsin, Tatiana Abrão Jana, and Leticia S. Marinho.

Regular publication venues for Ferrari's work include Biometrical Journal, arXiv (Cornell University), Behavioral Sciences, Statistical Papers, and Metrika.

Recent papers authored or coauthored by Ferrari cover diverse applications within statistics and related fields:

  • An Examination of the Relationships between Eating-Disorder Symptoms, Difficulties with Emotion Regulation, and Mental Health in People with Binge Eating Disorder (2023, Behavioral Sciences)
  • Robust estimation in beta regression via maximum L$_q$-likelihood (2022, Statistical Papers)
  • Quantile modeling through multivariate log-normal/independent linear regression models with application to newborn data (2021, Biometrical Journal)
  • Power logit regression for modeling bounded data (2023, Statistical Modelling)
  • Robust beta regression through the logit transformation (2024, Metrika)

Best Publications

  • Beta Regression for Modelling Rates and Proportions

    Sílvia Lopes de Paula Ferrari;Francisco Cribari-Neto

  • A general class of zero-or-one inflated beta regression models

    Raydonal Ospina;Silvia L. P. Ferrari

  • Inflated beta distributions

    Raydonal Ospina;Silvia L. P. Ferrari

  • Smooth function approximation using neural networks

    S. Ferrari;R.F. Stengel

  • On beta regression residuals

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

  • Information-Driven Sensor Path Planning by Approximate Cell Decomposition

    Chenghui Cai;S. Ferrari

  • A modified score test statistic having chi-squared distribution to order n−1

    Gauss M. Cordeiro;Silvia L. De Paula Ferrari

  • Online Adaptive Critic Flight Control

    Silvia Ferrari;Robert F. Stengel

  • Influence diagnostics in beta regression

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

  • A constrained integration (CINT) approach to solving partial differential equations using artificial neural networks

    Keith Rudd;Silvia Ferrari

  • Deep learning feature extraction for target recognition and classification in underwater sonar images

    Pingping Zhu;Jason Isaacs;Bo Fu;Silvia Ferrari

  • Mixed beta regression

    Jorge I. Figueroa-Zúñiga;Reinaldo B. Arellano-Valle;Silvia L.P. Ferrari

  • Constructing Bayesian networks for criminal profiling from limited data

    K. Baumgartner;S. Ferrari;G. Palermo

  • A Mobile Sensing Approach for Regional Surveillance of Fugitive Methane Emissions in Oil and Gas Production

    John. D. Albertson;Tierney Harvey;Greg Foderaro;Pingping Zhu

  • Distributed optimal control for multi-agent trajectory optimization

    Greg Foderaro;Silvia Ferrari;Thomas A. Wettergren

  • An Information Roadmap Method for Robotic Sensor Path Planning

    G. Zhang;S. Ferrari;M. Qian

  • Improved heteroscedasticity‐consistent covariance matrix estimators

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

  • Improved Score Tests for Generalized Linear Models

    Gauss M. Cordeiro;Silvia L. de Paula Ferrari;Gilberto A. Paula

  • A Geometric Optimization Approach to Detecting and Intercepting Dynamic Targets Using a Mobile Sensor Network

    Silvia Ferrari;Rafael Fierro;Brent Perteet;Chenghui Cai

  • On the Bell distribution and its associated regression model for count data

    Fredy Castellares;Silvia L.P. Ferrari;Artur J. Lemonte

  • An adaptive critic global controller

    S. Ferrari;R.F. Stengel

Frequent Co-Authors

Francisco Cribari-Neto
Francisco Cribari-Neto Federal University of Pernambuco
Gauss M. Cordeiro
Gauss M. Cordeiro Federal University of Pernambuco
Rafael Fierro
Rafael Fierro University of New Mexico
Robert F. Stengel
Robert F. Stengel Princeton University
Craig S. Henriquez
Craig S. Henriquez Duke University
John D. Albertson
John D. Albertson Cornell University
Marc A. Sommer
Marc A. Sommer Duke University
Tobias Egner
Tobias Egner Duke University
Vahid Tarokh
Vahid Tarokh Duke University

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