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Though introduced nearly 50 years ago, the infinitesimal jackknife (IJ) remains a popular modern tool for quantifying predictive uncertainty in complex estimation settings.
On the asymptotic distribution of differentiable statistical functions
R v Mises · 1947
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The central limit theorem for dependent random variables
Wassily Hoeffding, Herbert Robbins, et al · 1948
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The 1972 wald lecture robust statistics: A review
Peter J Huber et al · 1972
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The infinitesimal jackknife
Louis A Jaeckel · 1972
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The influence curve and its role in robust estimation
Frank R Hampel · 1974
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The jackknife–a review
Rupert G. Miller · 1974
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Bootstrap methods: Another look at the jackknife
Bradley Efron · 1979
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Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods
Bradley Efron · 1981
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The jackknife, the bootstrap, and other resampling plans , volume 38
Bradley Efron · 1982
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Prepivoting test statistics: a bootstrap view of asymptotic refinements
Rudolf Beran · 1988
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An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani · 1994
Cited alongside, same era.
Large sample confidence regions based on subsamples under minimal assumptions
Dimitris N Politis and Joseph P Romano · 1994
Cited alongside, same era.
Bagging predictors
Leo Breiman · 1996
Cited alongside, same era.
Resampling fewer than n observations: gains, losses, and remedies for losses
Peter J Bickel, Friedrich Götze, and Willem R van Zwet · 1997
Cited alongside, same era.
Improving the reliability of bootstrap tests
Russel Davidson and James MacKinnon · 2000
Cited alongside, same era.
A reality check for data snooping
Halbert White · 2000
Cited alongside, same era.
Estimation and accuracy after model selection
Bradley Efron · 2014
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A scalable bootstrap for massive data
Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, and Michael I Jordan · 2014
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Confidence intervals for random forests: The jackknife and the infinitesimal jackknife
Stefan Wager, Trevor Hastie, and Bradley Efron · 2014
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Double-bootstrap methods that use a single double-bootstrap simulation
Jinyuan Chang and Peter Hall · 2015
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Quantifying uncertainty in random forests via confidence intervals and hypothesis tests
Lucas Mentch and Giles Hooker · 2016
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A subsampled double bootstrap for massive data
Srijan Sengupta, Stanislav Volgushev, and Xiaofeng Shao · 2016
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Russell Davidson and James G MacKinnon · 2002
Cited alongside, same era.
Improving the reliability of bootstrap tests with the fast double bootstrap
Russell Davidson and James G MacKinnon · 2007
Cited alongside, same era.
Standard errors for bagged and random forest estimators
Joseph Sexton and Petter Laake · 2009
Cited alongside, same era.
A warp-speed method for conducting monte carlo experiments involving bootstrap estimators
Raffaella Giacomini, Dimitris N Politis, and Halbert White · 2013
Cited alongside, same era.
Sopra le funzioni che dipendono da altre funzioni
Vito Volterra
Cited in the paper.
Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey
Cited in the paper.
Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey · 2017
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Wei Peng, Tim Coleman, and Lucas Mentch · 2019
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V-statistics and variance estimation
Zhengze Zhou, Lucas Mentch, and Giles Hooker · 2019
Later among the works it cites.