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  1. 30 de abr. de 2024 · by Yoav Benjamini, Richard De Veaux, Bradley Efron, Scott Evans, Mark Glickman, Barry I. Graubard, Xuming He, Xiao-Li Meng, and 7 more Published: Jul 30, 2021 Remembering Robert Lue: Giving Students “Not a Data Science Course, but a Data Science Life”

  2. 1 de mai. de 2024 · In 1979, Bradley Efron introduced a resampling technique called “The Bootstrap“. Bootstrapping allows you to estimate the distribution of a statistic (like mean, variance, etc.) by repeatedly sampling from the dataset with replacement. We call these repeated samples with replacement “bootstrap samples”.

  3. 20 de abr. de 2024 · Stein’s identities and the related topics: an instructive explanation on shrinkage, characterization, normal approximation and goodness-of-fit. Tatsuya Kubokawa. Original Paper. Open Access. Published: 31 January 2024.

  4. 28 de abr. de 2024 · Journal of the American Statistical Association, Theory and Methods, 117 (539), 1149-1166. Selected as a discussion paper by the editors of JASA. The discussion took place at JSM 2022. Discussants: Noel Cressie, Subhashis Ghosal, Peter Hoff, Bradley Efron, Guido Imbens, Marianna Pensky, Dongyue Xie & Matthew Stephens.

  5. Há 6 dias · Like many other academic professional societies, the American Statistical Association (ASA) uses the title of Fellow of the American Statistical Association as its highest honorary grade of membership.

  6. 29 de abr. de 2024 · Abstract. This is a writeup, with some elaboration, of the talks by the two authors (a physicist and a statistician) at the first PHYSTAT Informal review on January 24, 2024. We discuss Bayesian and frequentist approaches to dealing with nuisance parameters, in particular, integrated versus profiled likelihood methods.

  7. 22 de abr. de 2024 · Bradley Efron and Trevor Hastie. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Number 5 in Institute of Mathematical Statistics Monographs. Cambridge University Press, New York, 2016. ISBN 978-1-107-14989-2. URL: https://web.stanford.edu/~hastie/CASI/.