The Karl Pearson Prize was formerly named after one of the Founders of statistical science, whose many contributions include the chi-square test for goodness of fit, the principal component method of dimension reduction, and the method of moments for statistical estimation.
On the other hand, Pearson was an active eugenics proponent. While Pearson’s eugenic ideas are not surprising for his time, we reject them today, as we reject all discriminatory ideas. In coherence with ISI principles, we find it inappropriate to keep using Karl Pearson’s name in association with prizes, lectures and other awards. Therefore it was decided to rename the Prize and honor the many Founders of Statistics with this Prize.
The 2019 Karl Pearson Prize
was given to Yoav Benjamini.
Yoav Benjamini was awarded for his Benjamini-Hochberg 1995 paper “Controlling the false discovery rate: a practical and powerful approach to multiple testing” (J. Roy. Statist. Soc. Ser. B 57, 1995, no. 1, 289–300).
The paper by Benjamini and Hochberg, cited more than 50,000 times, introduced the false discovery rate or FDR that is widely used in diverse sciences to make simultaneous inference about a large number of hypotheses. FDR liberalizes the threshold for identifying hypotheses worth further investigation while at the same time controlling the rate of false discoveries. It has become an essential part of the analysis pipeline of complex data around the world. In addition to its wide applicability, the FDR paper includes elegant mathematical statistics.
- The False Discovery Rate | Stats + Stories Episode 108 with Yoav Benjamini
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- Thank you video message from Yoav Benjamini
The prize was presented at the 62nd ISI World Statistics Congress in Kuala Lumpur.
The 2017 Karl Pearson Prize
was given to Roderick J. Little and Donald B. Rubin.
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 21 July 2017 at the 61st ISI World Statistics Congress in Marrakech.
The 2015 Karl Pearson Prize
was given to Kung-Yee Liang and Scott Zeger.
The prize was awarded 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 31 July 2015 at the ISI World Statistics Congress in Rio de Janeiro, and was followed by the Karl Pearson Lecture by Scott Zeger.
The 2013 Karl Pearson Prize
was given to Peter McCullagh and John Nelder.
The inaugural Karl Pearson Prize was awarded 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 27 August 2013 at the ISI World Statistics Congress in Hong Kong, and was followed by the Karl Pearson Lecture by Peter McCullagh.
 John Nelder passed away in August 2010.