Bradley Efron
Stanford University · Statistics
Active 1964–2024
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
Empirical Bayes: Concepts and Methods
2024-01-22 · 11 citations
book-chapter1st authorCorrespondingRobbins (1956) coined the name “empirical Bayes” for his method of dealing with the following kind of inferential situation: an unknown probability density g(Θ) (“density” here including the possibility of discrete atoms) has produced a random sample of realizations Θ 1, Θ 2, . . . , ΘN ; the Θi are unobserved, but each one has yielded an observed random variable xi according to a known family of densities f(xi | Θi ): 2.1 https://www.w3.org/1998/Math/MathML" display="block"> Θ i ~ g ( Θ ) and x i ~ f ( x i ∣ Θ i ) https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429341731/7ae177d9-a70d-4244-bda5-0ab0fe307079/content/equ08-01.tif"/>
2023-04-03
preprintOpen access<p>siRNA loading into EVs</p>
2023-04-03
preprintOpen access<p>Methods/legend to Video 1</p>
2023-04-03
preprintOpen access<p>siRNA loading into EVs</p>
2023-04-03
preprintOpen access<p>EVHB protein sequence</p>
Machine learning and the James–Stein estimator
Japanese Journal of Statistics and Data Science · 2023-06-30 · 5 citations
articleOpen access1st authorCorrespondingAbstract It is now 62 years since the publication of James and Stein’s seminal article on the estimation of a multivariate normal mean vector. The paper made a spectacular first impression on the statistical community through its demonstration of inadmissability of the maximum likelihood estimator. It continues to be influential, but not for the initial reasons. Empirical Bayes shrinkage estimation, now a major topic, found its early justification in the James–Stein formula. Less obvious downstream topics include Tweedie’s formula and Benjamini and Hochberg’s false discovery rate algorithm. This is a short and mainly non-technical review of the James–Stein rule and its effects on the machine learning era of statistical innovation.
2023-04-03
preprintOpen access<p>EV markers Western blot</p>
2023-04-03
preprintOpen access<div>Abstract<p>This paper deals with specific targeting of the prodrug/enzyme regimen, CNOB/HChrR6, to treat a serious disease, namely HER2<sup>+</sup> human breast cancer with minimal off-target toxicity. HChrR6 is an improved bacterial enzyme that converts CNOB into the cytotoxic drug MCHB. Extracellular vesicles (EV) were used for mRNA-based H<i>chrR6</i> gene delivery: EVs may cause minimal immune rejection, and mRNA may be superior to DNA for gene delivery. To confine HChrR6 generation and CNOB activation to the cancer, the EVHB chimeric protein was constructed. It contains high-affinity anti-HER2 scFv antibody (ML39) and is capable of latching on to EV surface. Cells transfected with EVHB-encoding plasmid generated EVs displaying this protein (“directed EVs”). Transfection of a separate batch of cells with the new plasmid, XPort/HChrR6, generated EVs containing HChrR6 mRNA; incubation with pure EVHB enabled these to target the HER2 receptor, generating “EXO-DEPT” EVs. EXO-DEPT treatment specifically enabled HER2-overexpressing BT474 cells to convert CNOB into MCHB in actinomycin D–independent manner, showing successful and specific delivery of HChrR6 mRNA. EXO-DEPTs—but not undirected EVs—plus CNOB caused near-complete growth arrest of orthotopic BT474 xenografts <i>in vivo</i>, demonstrating for the first time EV-mediated delivery of functional exogenous mRNA to tumors. EXO-DEPTs may be generated from patients' own dendritic cells to evade immune rejection, and without plasmids and their potentially harmful genetic material, raising the prospect of clinical use of this regimen. This approach can be used to treat any disease overexpressing a specific marker. <i>Mol Cancer Ther; 17(5); 1133–42. ©2018 AACR</i>.</p></div>
Harvard Data Science Review · 2023-04-27 · 2 citations
articleOpen access1st authorCorresponding2023-04-03
preprintOpen access<p>Methods/legend to Video 1</p>
Recent grants
Statistical Theory and Methodology
NSF · $725k · 2000–2005
NIH · $557k · 2008
Statistical Theory and Methodology
NSF · $310k · 2005–2008
NIH · $1.9M · 2016
Statistical Theory and Methodology
NSF · $500k · 2012–2016
Frequent coauthors
- 57 shared
Trevor Hastie
- 51 shared
Robert Tibshirani
- 20 shared
Balasubramanian Narasimhan
- 19 shared
Carl N. Morris
University of Central Lancashire
- 15 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
Awards & honors
- 2005 National Medal of Science
- 2014 Guy Medal in Gold by the Royal Statistical Society
- 2018 International Prize in Statistics
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