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We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods.
The infinitesimal jackknife
Louis A Jaeckel · 1972
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Some comments on Cp
Colin L Mallows · 1973
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The jackknife estimate of variance
Bradley Efron and Charles Stein · 1981
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Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate. II. radical prostatectomy treated patients
Thomas A Stamey, John N Kabalin, John E McNeal, Iain M Johnstone, Fuad Freiha, EA Redwine, and N Yang · 1989
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Compliance as an explanatory variable in clinical trials
Bradley Efron and David Feldman · 1991
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Jackknife-after-bootstrap standard errors and influence functions
Bradley Efron · 1992
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Bagging predictors
Leo Breiman · 1996
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Bagging for linear classifiers
Marina Skurichina and Robert PW Duin · 1998
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An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
Thomas G Dietterich · 2000
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Random forests
Leo Breiman · 2001
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Analyzing bagging
Peter Bühlmann and Bin Yu · 2002
Cited alongside, same era.
Stochastic gradient boosting
Jerome H Friedman · 2002
Cited alongside, same era.
Classification and regression by randomForest
Andy Liaw and Matthew Wiener · 2002
Cited alongside, same era.
Modern Applied Statistics with S
William N Venables and Brian D Ripley · 2002
Cited alongside, same era.
Effects of bagging and bias correction on estimators defined by estimating equations
Song Xi Chen and Peter Hall · 2003
Cited alongside, same era.
Observations on bagging
Andreas Buja and Werner Stuetzle · 2006
Cited alongside, same era.
On bagging and nonlinear estimation
Jerome H Friedman and Peter Hall · 2007
Later among the works it cites.
Bias in random forest variable importance measures: Illustrations, sources and a solution
Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn · 2007
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Consistency of random forests and other averaging classifiers
Gérard Biau, Luc Devroye, and Gábor Lugosi · 2008
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The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Standard errors for bagged and random forest estimators
Joseph Sexton and Petter Laake · 2009
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Bootstrap-Based Variance Estimators for A Bagging Predictor
Jiangtao Duan · 2011
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Pierre Geurts, Damien Ernst, and Louis Wehenkel · 2006
Cited alongside, same era.
Random forests and adaptive nearest neighbors
Yi Lin and Yongho Jeon · 2006
Cited alongside, same era.
Quantile regression forests
Nicolai Meinshausen · 2006
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Analysis of a random forests model
Gérard Biau · 2012
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Model selection, estimation, and bootstrap smoothing
Bradley Efron · 2012
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UCI machine learning repository, 2013
Kevin Bache and Moshe Lichman · 2013
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