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Pearson Award
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The Karl Pearson Prize for Contemporary Research Contribution

 

Prize

The Karl Pearson Prize recognizes a research contribution that has had profound influence on statistical theory, methodology, practice, or applications. The contribution must be a research article or book published within the last three decades. The prize is given biennially, at the ISI World Statistics Congress (WSC), starting with the WSC in Hong Kong in August 2013. It comprises a cash award of 5,000 euros, and the winner presents the Karl Pearson Lecture at the WSC. If the contribution has multiple authors, the cash prize will be divided equally, and travel support will be provided for one of the authors to attend the WSC and present the lecture.

The ISI is grateful to Elsevier Publishers for sponsoring the prize and for covering travel expenses to the WSC to present the lecture.

Criteria

  • The article or book must have been published within the last 30 years.
  • The publication must be in English.
  • It must be a stand-alone research contribution that has had major influence in one or more of the following:
    • Statistical theory
    • Statistical methodology
    • Statistical practice
    • Application areas

Submitting nominations

The next Call for Nominations will be published during 2018.
[Sample CfN: The Call for Nominations for the 2017 Karl Pearson Prize including submitting instructions can be viewed here.]

 

Karl Pearson (1857-1936), a mathematician and philosopher, was a key figure in the development of Mathematical Statistics.  His contributions include: the correlation coefficient; the Chi-squared statistic for testing goodness-of-fit and for measuring association in contingency tables; the method of moments; the Pearson family of frequency curves, and a flexible class of distributions. His 1901 paper laid the foundation for principal component analysis, the technique for dimension reduction in multivariate analysis. Pearson's statistical tables and the material explaining their use became the first advanced texts for the new methodology. His work also covered applications to biology, epidemiology, anthropometry, medicine, and social history. He wrote Grammar of Science, a foundational book with an empirical approach to the philosophy of science.
Pearson was also a builder of institutions. He began the program of advanced instruction in statistics at University College London, attracting students from all over the world who in turn helped to develop statistics curricula in their own countries. He co-founded the journal Biometrika in 1901 and edited it until his death.
 

 

 

 

 

 

 

 

 

 

 

 

 

The 2017 Karl Pearson Prize was awarded to Roderick J. Little and Donald B. Rubin for their book "Statistical Analysis With Missing Data", published by John Wiley & Sons (1987). 

The work of Roderick J. Little and Donald B. Rubin, laid out in their seminal 1978 Biometrika papers and 1987 book, updated in 2002, has been no less than defining and transforming. Earlier missing data work was ad hoc at best. Little and Rubin defined the field and provided the methodological and applied communities with a useful and usable taxonomy and a set of key results. Today, their terminology and methodology is used more than ever. Their work has been transforming for the deep impact it had and has on both statistical practice and theory. It is one of the rare topics that has continued for the past thirty years to be studied and developed in academia, government and industry. For example, it plays a key role in the current work on sensitivity analysis with incomplete data.

The prize was presented on July 21, 2017 at the 61st ISI World Statistics Congress in Marrakech. Rod Little gave the Karl Pearson Lecture on Friday morning, 20 July.

 

Roderick J. Little

 

Donald B. Rubin

 


The 2015 Karl Pearson Prize was awarded to Kung-Yee Liang and Scott Zeger for their paper “Longitudinal data analysis using generalized linear models” published in Biometrika (1986).

This paper had an immediate and sustained impact on both theory and methodology in statistics and biostatistics, as well as on applications in medical, physical and social sciences.  In the early 1980’s, inference using generalized linear models was enabling regression methods to be quickly adapted to models and data with non-normal responses.  At the same time the collection of repeated measurements on the same individual was a prominent feature of work in social sciences, medicine, public health, and other areas of science.  Liang and Zeger showed how to adapt the generalized linear models framework to these settings, using methodology they proposed under the name generalized estimating equations (GEE). This methodology is now a staple component of applied statistics courses, of statistical computing packages, and of hundreds upon hundreds of analyses in diverse subject matter fields.  The theoretical basis for the approach has been refined, and extended, to encompass a wide range of models with complex dependencies.  The paper was included in the 1997 volume of Breakthroughs in Statistics, accompanied by a comprehensive overview by Peter Diggle.

The prize was presented on July 31, 2015 at the ISI World Statistics Congress in Rio de Janeiro and was followed by the Karl Pearson Lecture by Scott Zeger.

 

Kung-Yee Liang

 

Scott Zeger

 


The inaugural Karl Pearson Prize was awarded to Peter McCullagh and John Nelder[1] for their monograph Generalized Linear Models (1983).

This book has changed forever teaching, research and practice in statistics. It provides a unified and self-contained treatment of linear models for analyzing continuous, binary, count, categorical, survival, and other types of data, and illustrates the methods on applications from different areas. The monograph is based on several groundbreaking papers, including “Generalized linear models,” by Nelder and Wedderburn, JRSS-A (1972), “Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method,” by Wedderburn, Biometrika (1974), and “Regression models for ordinal data,” by P. McCullagh, JRSS-B (1980). The implementation of GLM was greatly facilitated by the development of GLIM, the interactive statistical package, by Baker and Nelder. In his review of the GLIM3 release and its manual in JASA 1979 (pp. 934-5), Peter McCullagh wrote that "It is surprising that such a powerful and unifying tool should not have achieved greater popularity after six or more years of existence.” The collaboration between McCullagh and Nelder has certainly remedied this issue and has resulted in a superb treatment of the subject that is accessible to researchers, graduate students, and practitioners.

The prize was presented on August 27, 2013 at the ISI World Statistics Congress in Hong Kong and was followed by the Karl Pearson Lecture by Peter McCullagh.



[1] John Nelder passed away in August 2010.

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