World's Best Scientists 2026 revealed!
Award Badge
Mathematics
USA
2026

D-Index & Metrics

Mathematics

D-Index
74
Citations
70105
World Ranking
206
National Ranking
118

Alan Agresti 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 Alan Agresti 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: 199 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Alan Agresti 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 Alan Agresti 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: 74 D-Index — 94th percentile

94% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 1990 - Fellow of the American Statistical Association (ASA)

Overview

Alan Agresti is affiliated with the University of Florida in the United States. Their research spans several key areas within mathematics and computer science, with a strong focus on statistics and probability as the primary subfield. The body of work demonstrates significant contribution to statistical methods, Bayesian inference, and data analysis.

The main fields of study for Alan Agresti include:

  • Mathematics
  • Computer Science

The primary subfields explored by Agresti are:

  • Statistics and Probability
  • Artificial Intelligence
  • Computer Networks and Communications
  • General Agricultural and Biological Sciences

The main topics of their research encompass:

  • Statistical Methods and Bayesian Inference
  • Advanced Statistical Methods and Models
  • Statistical Methods and Inference
  • Statistics Education and Methodologies
  • Bayesian Modeling and Causal Inference
  • Probability and Statistical Research
  • Data Analysis with R

Alan Agresti has published in several academic venues, reflecting a range of statistical science topics. Notable publication venues include:

  • Brazilian Journal of Probability and Statistics
  • Scandinavian Journal of Statistics
  • Journal of Quantitative Economics
  • Statistical Modelling
  • Journal of the American Statistical Association

Recent papers feature detailed explorations in statistical science and categorical data inference. Selected works include:

  • The foundations of statistical science: A history of textbook presentations (2021, Brazilian Journal of Probability and Statistics)
  • A historical overview of textbook presentations of statistical science (2023, Scandinavian Journal of Statistics)
  • A Review of Score-Test-Based Inference for Categorical Data (2022, Journal of Quantitative Economics)
  • Reflections on Murray Aitkin's contributions to nonparametric mixture models and Bayes factors (2021, Statistical Modelling)
  • Confidence Intervals for Discrete Data in Clinical Research (2023, Journal of the American Statistical Association)

Frequent co-authors in their research collaborations include:

  • Claudia Tarantola
  • Roberta Varriale
  • Maria Kateri
  • Sabrina Giordano
  • Anna Gottard

In recognition of their work, Alan Agresti was named Fellow of the American Statistical Association in 1990.

Best Publications

  • Categorical data analysis

    Alan Agresti

  • An introduction to categorical data analysis

    Alan Agresti

  • Statistical Methods for the Social Sciences

    Alan Agresti;Barbara Finlay

  • Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions

    Alan Agresti;Brent A. Coull

  • Analysis of ordinal categorical data

    Alan Agresti

  • A Survey of Exact Inference for Contingency Tables

    Alan Agresti

  • Foundations of Linear and Generalized Linear Models

    Alan Agresti

  • Simple and Effective Confidence Intervals for Proportions and Differences of Proportions Result from Adding Two Successes and Two Failures

    Alan Agresti;Brian Caffo

  • Categorical Data Analysis

    Unknown

  • Random effect models for repeated measures of zero-inflated count data:

    Yongyi Min;Alan Agresti

  • Statistical Analysis of Qualitative Variation

    Alan Agresti;Barbara F. Agresti

  • The analysis of ordered categorical data: An overview and a survey of recent developments

    Ivy Liu;Alan Agresti

  • Summarizing the predictive power of a generalized linear model.

    Beiyao Zheng;Alan Agresti

  • Simultaneously Modeling Joint and Marginal Distributions of Multivariate Categorical Responses

    Joseph B. Lang;Alan Agresti

  • Statistical models for ordinal variables

    Alan Agresti;Clifford C. Clogg;Edward S. Shihadeh

  • Random-Effects Modeling of Categorical Response Data

    Alan Agresti;James G Booth;James P Hobert;Brian S Caffo

  • Multinomial logit random effects models

    Jonathan Hartzel;Alan Agresti;Brian S Caffo

  • Introduction to Generalized Linear Models

    Alan Agresti

  • Statistics: The Art and Science of Learning from Data

    Alan Agresti;Christine A. Franklin

  • Categorical Data Analysis.

    G. J. G. Upton;A. Agresti

  • Categorical Data Analysis.

    Dennis Lendrem;A. Agresti

  • Analysis of Ordinal Categorical Data.

    R. L. Plackett;A. Agresti

  • Teacher's Corner Simple and Effective Confidence Intervals for Proportions and Differences of Proportions Result from Adding Two Successes and Two Failures

    Alan Agresti;Brian Caffo

Frequent Co-Authors

Brent A. Coull
Brent A. Coull Harvard University
Myles Hollander
Myles Hollander Florida State University
Cyrus R. Mehta
Cyrus R. Mehta Cytel (United States)
Ming-Hui Chen
Ming-Hui Chen University of Connecticut
Malay Ghosh
Malay Ghosh University of Florida
Roderick J. A. Little
Roderick J. A. Little University of Michigan–Ann Arbor
James M. Boyett
James M. Boyett St. Jude Children's Research Hospital
Stuart R. Lipsitz
Stuart R. Lipsitz Brigham and Women's Hospital
Jeffrey S. Simonoff
Jeffrey S. Simonoff New York University
Leslie C. Morey
Leslie C. Morey Texas A&M University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students studying Mathematics in the USA, exploring related online degrees can open up diverse career pathways. Many graduates find that combining mathematical skills with business knowledge enhances their job prospects. For those interested in advancing into managerial roles, discovering mba transfer credits options can ease the transition into top online MBA programs, especially if they already have some graduate-level coursework.

Data analytics is one of the fastest-growing fields where math graduates excel. Enrolling in one of the best masters in data analytics programs offers practical experience and advanced analytics skills that are highly sought after in industries like finance, tech, and healthcare.

Students concerned about accessibility and admission requirements might explore what mba programs can i get into to find suitable programs. Fortunately, the increasing availability of flexible formats makes it easier than ever to pursue further education while balancing other commitments.

For those seeking a streamlined path, identifying the easiest online mba programs can provide a manageable entry point to enhance both leadership and quantitative skills without overwhelming workloads.

Best Scientists Citing Alan Agresti

Trending Scientists

Recently Published Articles