World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
41
Citations
37823
World Ranking
1834
National Ranking
43

Research.com Recognitions

  • 1984 - Fellow of the American Statistical Association (ASA)

Overview

Murray Aitkin is affiliated with the University of Melbourne in Australia. Their research primarily focuses on fields related to Mathematics, with a particular emphasis on Statistics and Probability as well as Artificial Intelligence.

The main topics covered in their work include:

  • Statistical Methods and Bayesian Inference
  • Data Analysis with R
  • Statistical Methods in Clinical Trials
  • Advanced Statistical Methods and Models

Some of Murray Aitkin's recent papers are as follows:

  • Reflections on statistical modelling: A conversation with Murray Aitkin, 2021, Statistical Modelling
  • The flaw of averages: Bayes factors as posterior means of the likelihood ratio, 2024, Pharmaceutical Statistics

Throughout their career, they have collaborated with a number of co-authors, including:

  • John Hinde
  • Brian Francis
  • Charles C. Liu
  • Ron Yu

Their work has appeared in various publication venues, notably:

  • Statistical Modelling
  • Pharmaceutical Statistics

Murray Aitkin has been recognized as a Fellow of the American Statistical Association (ASA) since 1984.

Best Publications

  • The Statistical Analysis of Failure Time Data.

    Murray Aitkin;J. D. Kalbfleisch;R. L. Prentice

  • Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm.

    R. Darrell Bock;Murray Aitkin

  • Statistical Modelling in GLIM

    Murray Aitkin;Dorothy Anderson;Brian Francis;John Hinde

  • Statistical Modelling Issues in School Effectiveness Studies

    M. Aitkin;N. Longford

  • Statistical Modelling of Data on Teaching Styles

    Murray Aitkin;Dorothy Anderson;John Hinde

  • Marginal maximum likelihood estimation of item parameters

    R. D. Bock;M. Aitkin

  • Posterior Bayes Factors

    Murray Aitkin

  • A METHOD OF FITTING THE GRAVITY MODEL BASED ON THE POISSON DISTRIBUTION

    Robin Flowerdew;Murray Aitkin

  • Estimation and Hypothesis Testing in Finite Mixture Models

    Murray Aitkin;Donald B. Rubin

  • A general maximum likelihood analysis of variance components in generalized linear models.

    Murray Aitkin

  • Variance Component Models with Binary Response: Interviewer Variability

    Dorothy A. Anderson;Murray Aitkin

  • The Fitting of Exponential, Weibull and Extreme Value Distributions to Complex Censored Survival Data using GLIM

    Murray Aitkin;David Clayton

  • Modelling variance heterogeneity in normal regression using GLIM

    Murray Aitkin

  • Mixture Models, Outliers, and the EM Algorithm

    Murray Aitkin;Granville Tunnicliffe Wilson

  • A general maximum likelihood analysis of overdispersion in generalized linear models

    Murray Aitkin

  • Statistical modelling in R

    Murray Aitkin;Brian Francis;John Hinde;Ross Darnell

  • Bayes factors: Prior sensitivity and model generalizability

    Charles C. Liu;Murray Aitkin

  • Simultaneous Inference and the Choice of Variable Subsets in Multiple Regression

    Murray A. Aitkin

  • TEACHING STYLES AND PUPIL PROGRESS: A RE‐ANALYSIS

    M. Aitkin;S. N. Bennett;Jane Hesketh

  • Statistical Modelling in GLIM.

    Daniel C. Coster;Murray Aitken;Dorothy Anderson;Brian Francis

  • Statistical modelling issues in school effectiveness studies.

    M. Aitkin

  • Statistical Modelling in GLIM.

    Yoga Sittampalam;M. Aitkin;D. Anderson;B. Francis

Frequent Co-Authors

Brian Francis
Brian Francis Lancaster University
Ann K. Daly
Ann K. Daly Newcastle University
Margaret F. Bassendine
Margaret F. Bassendine Newcastle University
Alan W. Craft
Alan W. Craft Newcastle University
Ross L. Prentice
Ross L. Prentice Fred Hutchinson Cancer Research Center
Donald B. Rubin
Donald B. Rubin Temple University
John D. Kalbfleisch
John D. Kalbfleisch University of Michigan–Ann Arbor

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

Pursuing a Mathematics degree in the USA opens doors to diverse career pathways, many of which can be enhanced through specialized online programs. For example, transitioning into data-driven roles often requires advanced skills, which can be developed through analytics masters programs. These programs focus on data analysis, predictive modeling, and statistical techniques that complement a strong mathematical foundation.

For those interested in leadership and business management, combining math expertise with an MBA can be a strategic move. Students seeking the quickest way to obtain these qualifications may consider the easiest mba program to get into, which balances accessibility with quality education. Additionally, options like the easiest and fastest online mba programs provide flexible formats for working professionals aiming to accelerate their careers.

Doctorate-level career paths are also available, especially for those interested in research or educational leadership. The cheapest aacsb online dba programs offer an affordable route to earning a Doctorate in Business Administration, incorporating analytical and managerial skills that build on a math background.

Overall, exploring these related online degrees can significantly expand career opportunities for mathematics graduates, aligning with various professional goals and lifestyles.

Best Scientists Citing Murray Aitkin

Trending Scientists

Recently Published Articles