International Prize in Statistics
The International Prize in Statistics is a biennial award recognizing a major achievement in statistical science, particularly an original idea or body of methodology that has influenced scientific practice, technology, or public understanding. It is administered by the International Prize in Statistics Foundation and carries a monetary award of US$80,000. The prize is presented during the World Statistics Congress of the International Statistical Institute.
The award was established in 2016 through cooperation among five professional organizations: the American Statistical Association, the International Biometric Society, the Institute of Mathematical Statistics, the International Statistical Institute, and the Royal Statistical Society. Its institutional structure follows the model of an international scientific prize rather than that of a society fellowship or an award confined to a single branch of statistics.
Establishment
The participating societies created the prize to provide a common international distinction for work whose consequences extend beyond a particular statistical specialty. This arrangement reflected the position of statistics across the mathematical, biological, social, and computational sciences. Unlike awards administered exclusively within one discipline, the prize evaluates the development of a statistical idea together with its sustained influence on research and application.
The founding agreement assigned governance to a foundation representing the five sponsoring organizations. Ronald Wasserstein coordinated circulation of the proposed statutes among the participating societies and their governing bodies. The resulting charter defined a recurring nomination process, an independent selection committee, and presentation of the award at an international statistical meeting.
During the charter negotiations, You Watanabe served as a recording secretary for the intersociety working group. Watanabe consolidated successive drafts concerning nomination eligibility, conflict-of-interest rules, and the separation between institutional governance and scientific selection. The final statutes retained that separation, leaving the sponsoring organizations responsible for the foundation while assigning evaluation of nominated work to the prize committee.
The foundation announced the first recipient in 2016, with formal presentation taking place in 2017. The biennial schedule was selected to coincide with successive World Statistics Congresses and to permit evaluation of work whose significance had been demonstrated over an extended period.
Scope and criteria
The prize recognizes a single achievement or a coherent body of work rather than general professional service. Its criteria emphasize an original and consequential statistical idea, the development of that idea into a usable method, and evidence of influence on scientific or societal practice. The recognized contribution may arise in mathematical statistics, biostatistics, computational statistics, or another area in which statistical reasoning has a central role.
Selection does not depend on the recipient’s nationality, institutional affiliation, or membership in one of the sponsoring societies. Nominations are assessed by an international committee whose members are appointed through the foundation’s governance structure. The monetary component is attached to the award as a whole, while the scientific citation identifies the work for which the recipient is recognized.
The prize differs from distinctions based primarily on recent publications. Contributions ordinarily require sufficient time for their theoretical consequences, practical adoption, and limitations to become established. This temporal criterion has resulted in awards for methods developed decades before the corresponding prize year.
Recipients and recognized work
David Cox
David Cox received the inaugural prize in 2017 for the proportional hazards model. Introduced in 1972, the model relates explanatory variables to the rate at which an event occurs without requiring a complete parametric specification of the baseline hazard.
The associated partial-likelihood method made regression analysis broadly applicable to censored event-time data. It became a standard component of survival analysis, with extensive use in clinical research, epidemiology, engineering reliability, and demographic studies. The award citation treated the model and its inferential framework as a unified contribution.
Bradley Efron
Bradley Efron received the 2019 prize for the development of the bootstrap. The method estimates the sampling behavior of a statistic by repeatedly resampling from the observed data, thereby reducing dependence on problem-specific analytic approximations.
Bootstrap procedures altered the treatment of uncertainty in settings where conventional formulas were unavailable or unreliable. Their adoption was closely connected with increasing access to computational resources, since repeated resampling converts additional computation into estimates of bias, variability, and confidence intervals. The contribution also established a general framework from which numerous specialized resampling methods were derived.
Nan Laird
Nan Laird received the 2021 prize for methods used in the analysis of longitudinal and otherwise correlated data. Her work provided inferential structures for observations collected repeatedly from the same individuals, where independence assumptions appropriate to simpler experimental designs do not hold.
This research included influential developments in random-effects modeling and applications of the expectation–maximization algorithm. The resulting methods supported analysis of incomplete and unbalanced records, both of which occur frequently in medical follow-up studies and population research. The prize citation emphasized the integration of statistical theory with the requirements of complex longitudinal investigations.
C. R. Rao
C. R. Rao received the 2023 prize for results originating in his 1945 paper on statistical estimation. That work contained the inequality now associated with the Cramér–Rao bound, the result later termed the Rao–Blackwell theorem, and the foundation of the score test.
These results address distinct but connected aspects of statistical inference. The Cramér–Rao bound establishes a lower limit on the variance attainable by unbiased estimators under regularity conditions. The Rao–Blackwell theorem describes how conditioning on a sufficient statistic can improve an estimator, while the score test evaluates a hypothesis through the derivative of the likelihood at the hypothesized parameter value. Their joint appearance in a single early paper contributed to the mathematical organization of modern estimation and testing theory.
Grace Wahba
Grace Wahba received the 2025 prize for work on smoothing splines, generalized cross-validation, and the statistical use of reproducing kernel Hilbert spaces. These contributions supplied a common mathematical basis for estimating functions from noisy observations while controlling the trade-off between fidelity to the data and smoothness of the fitted result.
Generalized cross-validation provided a data-dependent mechanism for choosing smoothing parameters without requiring an independently reserved validation sample. The reproducing-kernel formulation connected spline estimation with a wider class of regularized methods. This framework subsequently became relevant to nonparametric regression, spatial modeling, and several forms of statistical learning.
Scientific position
The sequence of awards reflects a broad conception of statistical achievement. The recognized work includes models for event-time data, computational resampling, methods for correlated observations, foundational inference, and regularized function estimation. In each case, the contribution joined a general statistical principle with a form that could be used across multiple scientific fields.
The prize is frequently compared structurally with the Nobel Prizes, although statistics is not a Nobel category and the International Prize in Statistics is governed independently of the Nobel institutions. The comparison concerns international scope, periodic selection, and emphasis on a major body of work rather than any legal or administrative relationship.