Bradley Efron

About

Brad is Professor Emeritus of Statistics in the School of Humanities and Sciences and Professor Emeritus of Biostatistics with the Department of Biomedical Data Science in the School of Medicine; he serves as Co-director of the Mathematical and Computational Sciences Program.

Research topics

• Computer Science
• Political Science
• Natural Language Processing
• Econometrics
• Statistics
• Economics
• Mathematics
• Law
• Management

Selected publications

• Prediction, Estimation, and Attribution

  Journal of the American Statistical Association · 2020-04-02 · 157 citations

  article1st authorCorresponding

  The scientific needs and computational limitations of the twentieth century fashioned classical statistical methodology. Both the needs and limitations have changed in the twenty-first, and so has the methodology. Large-scale prediction algorithms—neural nets, deep learning, boosting, support vector machines, random forests—have achieved star status in the popular press. They are recognizable as heirs to the regression tradition, but ones carried out at enormous scale and on titanic datasets. Ho…

  PublisherDOI

• The Automatic Construction of Bootstrap Confidence Intervals

  Journal of Computational and Graphical Statistics · 2020-01-14 · 93 citations

  articleOpen access1st authorCorresponding

  for nominal 95% two-sided coverage, are familiar and easy to use, but can be of dubious accuracy in regular practice. Bootstrap confidence intervals offer an order of magnitude improvement-from first order to second order accuracy. This paper introduces a new set of algorithms that automate the construction of bootstrap intervals, substituting computer power for the need to individually program particular applications. The algorithms are described in terms of the underlying theory that motivates…

  PublisherOA PDFDOI

• Prediction, Estimation, and Attribution

  International Statistical Review · 2020-12-01 · 78 citations

  article1st authorCorresponding

  Summary The scientific needs and computational limitations of the twentieth century fashioned classical statistical methodology. Both the needs and limitations have changed in the twenty‐first, and so has the methodology. Large‐scale prediction algorithms—neural nets, deep learning, boosting, support vector machines, random forests—have achieved star status in the popular press. They are recognizable as heirs to the regression tradition, but ones carried out at enormous scale and on titanic data…

  PublisherDOI

• The ASA president’s task force statement on statistical significance and replicability

  The Annals of Applied Statistics · 2021 · 40 citations

  Over the past decade, the sciences have experienced elevated concerns about the replicability of study results. An important aspect of replicability is the use of statistical methods for framing conclusions. In 2019 the President of the American Statistical Association (ASA) established a task force to address concerns that a 2019 editorial in The American Statisti cian (an ASA journal) might be mistakenly interpreted as official ASA policy. (The 2019 editorial recommended eliminating the use of…

  PublisherOA PDFDOI

• Exponential Families in Theory and Practice

  Cambridge University Press eBooks · 2022-10-31 · 28 citations

  book1st authorCorresponding

  During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting th…

  PublisherDOI

Recent grants

• Statistical Theory and Methodology

  NSF · $725k · 2000–2005

• NIH Grant R01EB002784

  NIH · $557k · 2008

• Statistical Theory and Methodology

  NSF · $310k · 2005–2008

Frequent coauthors

• Trevor Hastie

  57 shared

• Robert Tibshirani

  51 shared

• Balasubramanian Narasimhan

  20 shared

• Carl N. Morris

  University of Central Lancashire

  19 shared

• Mark D. Pegram

  Palo Alto University

  15 shared

• Travis J. Antes

  Cedars-Sinai Smidt Heart Institute

  15 shared

• Jing‐Hung Wang

  15 shared

• Daniel O. Frimannsson

  15 shared

Awards & honors

• 2005 National Medal of Science
• 2014 Guy Medal in Gold by the Royal Statistical Society
• 2018 International Prize in Statistics

Similar researchers at Stanford University

• David Donohoh-119
• Emmanuel J. Candèsh-114
• Jerome H. Friedmanh-103