World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
48
Citations
13404
World Ranking
1188
National Ranking
30

Anthony N. Pettitt 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 Anthony N. Pettitt 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: 187 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Anthony N. Pettitt 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 Anthony N. Pettitt 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: 48 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Anthony N. Pettitt is affiliated with the Queensland University of Technology in Australia. Their research spans several interdisciplinary fields, predominantly Biochemistry, Genetics and Molecular Biology, Mathematics, and Computer Science.

The scientist's key areas of study include Molecular Biology, Genetics, Statistics and Probability, Artificial Intelligence, and Modeling and Simulation. Their work focuses on topics such as gene expression and cancer classification, genetic associations and epidemiology, bioinformatics and genomic networks, genetic and phenotypic traits in livestock, Markov chains and Monte Carlo methods, Gaussian processes and Bayesian inference, and statistical methods with Bayesian inference.

Anthony N. Pettitt has contributed to several publications in notable venues. These include:

  • Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure (2020), published in Statistics in Medicine
  • Comparisons of statistical distributions for cluster sizes in a developing pandemic (2022), published in BMC Medical Research Methodology
  • Using a Supervised Principal Components Analysis for Variable Selection in High-Dimensional Datasets Reduces False Discovery Rates (2025), published in Statistics in Medicine
  • Using a supervised principal components analysis for variable selection in high-dimensional datasets reduces false discovery rates (2020), published in bioRxiv (Cold Spring Harbor Laboratory)
  • Analyse de la pléiotropie dans les GWAS à l'aide de méthodes bayésiennes prenant en compte la structure de groupe de variables (2021), published in Revue d Épidémiologie et de Santé Publique

Frequently collaborating with other researchers, Anthony N. Pettitt has worked alongside Kerrie Mengersen, Benoît Liquet, Taban Baghfalaki, Pierre-Emmanuel Sugier, and Thérèse Truong.

Their publications appear most often in journals such as Statistics in Medicine, BMC Medical Research Methodology, bioRxiv (Cold Spring Harbor Laboratory), Revue d'Épidémiologie et de Santé Publique, and arXiv (Cornell University).

Best Publications

  • A Non-Parametric Approach to the Change-Point Problem

    A. N. Pettitt

  • Importance Nested Sampling and the MultiNest Algorithm

    Farhan Feroz;Michael P. Hobson;Ewan Cameron;Anthony N. Pettitt

  • An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants

    Jesper Moller;Anthony N. Pettitt;Robert W. Reeves;Kasper K. Berthelsen

  • Marginal likelihood estimation via power posteriors

    Nial Friel;Anthony N. Pettitt

  • Model-based geostatistics. Discussion. Authors' reply

    P. J. Diggle;J. A. Tawn;R. A. Moyeed;R. Webster

  • A Review of Modern Computational Algorithms for Bayesian Optimal Design

    Elizabeth Gabrielle Ryan;Elizabeth Gabrielle Ryan;Christopher Drovandi;James McGree;Anthony Pettitt

  • A two-sample Anderson-Darling rank statistic

    A. N. Pettitt

  • Nonparametric Methods in General Linear Models.

    A. N. Pettitt;M. L. Puri;P. K. Sen

  • Estimation of parameters for macroparasite population evolution using approximate bayesian computation.

    C. C. Drovandi;A. N. Pettitt;A. N. Pettitt

  • A simple cumulative sum type statistic for the change-point problem with zero-one observations

    A. N. Pettitt

  • The Kolmogorov-Smirnov Goodness-of-Fit Statistic with Discrete and Grouped Data

    A. N. Pettitt;M. A. Stephens

  • Proportional Odds Models for Survival Data and Estimates Using Ranks

    A. N. Pettitt

  • Inference for the Linear Model Using a Likelihood Based on Ranks

    A. N. Pettitt

  • Modeling length of stay in hospital and other right skewed data: comparison of phase-type, gamma and log-normal distributions.

    Malcolm Faddy;Nicholas Graves;Nicholas Graves;Anthony Pettitt;Anthony Pettitt

  • A stochastic mathematical model of methicillin resistant Staphylococcus aureus transmission in an intensive care unit: predicting the impact of interventions.

    E.S. McBryde;A.N. Pettitt;D.L.S. McElwain

  • Modified Cramér-von Mises statistics for censored data

    A. N. Pettitt;M. A. Stephens

  • Approximate Bayesian Computation for astronomical model analysis: a case study in galaxy demographics and morphological transformation at high redshift

    E. Cameron;A. N. Pettitt

  • Sampling Designs for Estimating Spatial Variance Components

    A. N. Pettitt;A. B. Mcbratney

  • Approximate Bayesian computation using indirect inference

    Christopher C. Drovandi;Anthony N. Pettitt;Malcolm J. Faddy

  • Likelihood-free Bayesian estimation of multivariate quantile distributions

    Christopher C. Drovandi;Anthony N. Pettitt

  • Model-based geostatistics - Discussion

    Roberta Webster;Andrew Lawson;C Glasbey;G Horgan

  • Wiley Series in Probability and Statistics

    Clair L. Alston;Kerrie L. Mengersen;Anthony N. Pettitt

Frequent Co-Authors

Kerrie Mengersen
Kerrie Mengersen Queensland University of Technology
Andry Rakotonirainy
Andry Rakotonirainy Queensland University of Technology
Mark C. Bellingham
Mark C. Bellingham University of Queensland
Nicholas Graves
Nicholas Graves Queensland University of Technology
Ian Turner
Ian Turner Queensland University of Technology
D. M. Titterington
D. M. Titterington University of Glasgow
David Wilson
David Wilson Burnet Institute
Archie C. A. Clements
Archie C. A. Clements Queen's University Belfast
Michael A. Stephens
Michael A. Stephens Simon Fraser University
Sukumar Chakraborty
Sukumar Chakraborty Commonwealth Scientific and Industrial Research Organisation

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