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About the award

The Founders of Statistics Prize for Contemporary Research Contributions is given in honor of the Founders of Statistics. It 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). It comprises a cash award of 5,000 euros, and the winner presents the Founders of Statistics Prize Lecture at the WSC. If the contribution has multiple authors, the cash prize will be divided equally. The WSC 2021 will be a virtual congress and no travel support will therefore be provided. Complimentary registration fee is part of the prize.

The ISI is grateful to Elsevier Publishers for sponsoring the prize.

Submitting nominations

See the Call for Nominations for the 2021 prize

Former Karl Pearson Prize

The Founders of Statistics Prize was formerly named ‘Karl Pearson Prize’ 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.

2021 The Founders of Statistics Prize for Contemporary Research Contributions

Professor Robert Tibshirani is selected as the recipient of the 2021 ISI Founders of Statistics Prize for his 1996 paper on ‘Regression Shrinkage and Selection via the LASSO’, published in the Journal of the Royal Statistical Society Series B, pp. 267-288. Robert Tibshirani is Professor of Biomedical Science and Statistics at Stanford University.


Professor Robert Tibshirani Professor Robert Tibshirani

The Lasso paper (Tibshirani, 1996) has had 37,360 citations as of late January 2021 indicating its wide influence across statistics, data science, computer science and broadly in Science, Business and Economics. As we are faced with large and more complex data sets, the Lasso will continue to provide the basic underpinning for developments to handle such data. One reason for the paper’s impact is the clarity with which the paper is written with thorough and clear explanations of the method both analytically and geometrically. In statistics, biostatistics and machine learning, the Lasso (least absolute shrinkage and selection operator) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model. The Lasso is really the cornerstone of many modern methods of statistics and data science.

The contribution recognized by the Founders of Statistics Prize must be a research article or book published within the last three decades. Each Founders of Statistics (formerly known as Karl Pearson Prize) Award selection committee comprises renowned statisticians from across the world. The prize is given biennially, at the ISI World Statistics Congress (WSC), starting with the WSC in Hong Kong in August 2013. ISI is grateful to Elsevier Publishers for their ongoing sponsorship of the Prize.


2019 Karl Pearson Prize

The 2019 Karl Pearson Prize was awarded to Yoav Benjamini for the 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).


Prof. Joav Benjamini Yoav Benjamini

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.

See also:

The False Discovery Rate | Stats + Stories Episode 108 with Yoav Benjamini

ISI President's Tribute to 2019 Pearson Prize Winner Yoav Benjamini

Thank you video message from Yoav Benjamini


The prize was presented at the 62nd ISI World Statistics Congress in Kuala Lumpur.


2017 Karl Pearson Prize

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).


Roderick J. Little Roderick J. Little
Donald B. Rubin Donald B. Rubin

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. Rod Little gave the Karl Pearson Lecture on Friday morning, 20 July.


2015 Karl Pearson Prize

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).


Kung-Yee Liang Kung-Yee Liang
Scott Zeger Scott Zeger

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.


Inaugural: 2013 Karl Pearson Prize

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


Peter McCullagh Peter McCullagh
John Nelder John Nelder

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.

[1] John Nelder passed away in August 2010.