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Random forests remain among the most popular off-the-shelf supervised learning algorithms.
The accuracy of the gaussian approximation to the sum of independent variates
Andrew C Berry · 1941
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On the liapunov limit error in the theory of probability
Carl-Gustaf Esseen · 1942
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The theory of unbiased estimation
Paul R Halmos · 1946
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Wassily Hoeffding · 1948
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Wassily Hoeffding · 1961
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A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Charles Stein et al · 1972
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On the berry-esseen theorem for u-statistics
Y-K Chan and John Wierman · 1977
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The berry-esseen theorem for u u -statistics
Herman Callaert, Paul Janssen, et al · 1978
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The jackknife estimate of variance
B. Efron and C. Stein · 1981
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Classification and Regression Trees
Leo Breiman, Jerome Friedman, Charles J. Stone, and R.A. Olshen · 1984
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Infinite order u-statistics
Edward W Frees · 1989
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U-statistics: Theory and practice
Justin Lee · 1990
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Multivariate adaptive regression splines
Jerome H Friedman · 1991
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Bagging predictors
Leo Breiman · 1996
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Asymptotic Statistics
A. W. van der Vaart · 1998
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Random forests
Leo Breiman · 2001
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A non-uniform berry–esseen bound via stein’s method
Louis HY Chen and Qi-Man Shao · 2001
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Normal approximation under local dependence
Louis HY Chen, Qi-Man Shao, et al · 2004
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Random forests and adaptive nearest neighbors
Yi Lin and Yongho Jeon · 2006
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Quantile regression forests
Nicolai Meinshausen · 2006
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Confidence sets for split points in decision trees
Moulinath Banerjee and Ian W. McKeague · 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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Random survival forests
Hemant Ishwaran, Udaya B Kogalur, Eugene H Blackstone, Michael S Lauer, et al · 2008
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On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification
Gérard Biau and Luc Devroye · 2010
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Predicting the direction of stock market prices using random forest
Luckyson Khaidem, Snehanshu Saha, and Sudeepa Roy Dey · 2016
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A random forest guided tour
Gérard Biau and Erwan Scornet · 2016
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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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Random forests and kernel methods
Erwan Scornet · 2016
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Central limit theorems and bootstrap in high dimensions
Victor Chernozhukov, Denis Chetverikov, and Kengo Kato · 2017
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Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey · 2018
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Consistency of random survival forests
Hemant Ishwaran and Udaya B Kogalur · 2010
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Normal approximation by Stein’s method
Louis HY Chen, Larry Goldstein, and Qi-Man Shao · 2010
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Random forest for bioinformatics
Yanjun Qi · 2012
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Analysis of a random forests model
Gérard Biau · 2012
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Consistency of online random forests
Misha Denil, David Matheson, and Nando Freitas · 2013
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Do we need hundreds of classifiers to solve real world classification problems?
Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, and Dinani Amorim · 2014
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On nesting monte carlo estimators
Tom Rainforth, Rob Cornish, Hongseok Yang, Andrew Warrington, and Frank Wood · 2018
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Making sense of random forest probabilities: a kernel perspective
Matthew A Olson and Abraham J Wyner · 2018
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Consistency of survival tree and forest models: splitting bias and correction
Yifan Cui, Ruoqing Zhu, Mai Zhou, and Michael Kosorok · 2019
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Multiple data splitting for testing
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v v -statistics and variance estimation
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Scalable and efficient hypothesis testing with random forests
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Approximating high-dimensional infinite-order u u -statistics: Statistical and computational guarantees
Yanglei Song, Xiaohui Chen, and Kengo Kato · 2019
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Randomized incomplete u u -statistics in high dimensions
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Statistical inference on tree swallow migrations with random forests
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