Quantile Regression Forests

By Nicolai Meinshausen. 2006

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Citations in the corpus

Analysis of a Random Forests Model (2010, background): “…Jeon [32], who establish a connection between random forests and adaptive nearest neighbor methods (see also [5] for further results); Meinshausen [33], who studies the consistency of random forests in the context of conditional quantile prediction; and Devroye et al. [6], who oﬀer consistency…”
Invariant Causal Prediction for Nonlinear Models (2017, methodology): “For instance, a Quantile Regression Forest (Meinshausen, 2006) can be used here.”
Local Linear Forests (2018, methodology): “An alternate angle, advocated by Hothorn, Lausen, Benner, and Radespiel-Troger [2004], Meinshausen [2006], and Athey, Tibshirani, and Wager [2019], entails viewing random forests as adaptive weight generators.”
Generalized random forests (2016, methodology): “We compare our method to that of Meinshausen (2006) in Figure 2.”
Consistency of Random Forests (2014, background): “Breiman’s forests have also been extended to quantile estimation (Meinshausen, 2006), survival analysis (Ishwaran et al.”
Uniform Convergence of Random Forests via Adaptive Concentration (2015, methodology): “Ever since their introduction by Breiman [8], many authors have studied the theoretical properties of random forests in order to explain their performance [1, 4, 5, 9, 16, 28, 30, 35, 36, 37, 43].”
Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing (2021, methodology): “Similar results appear in Birmingham, Rotnitzky, and Fitzmaurice (2003), Robins, Rotnitzky, and Scharfstein (2000), Tan (2006), Graham (2011), Hristache and Patilea (2017), Franks, Amour, and Feller (2020), and Zhao, Small, and Bhattacharya (2019).”
Consistency of Random Forests and Other Averaging Classifiers (2008, background): “Meinshausen [15] proved consistency of certain random forests in the context of so-called quantile regression.”
Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem (2017, methodology): “Furthermore, various extensions of random forests—such as quantile regression forests (Meinshausen, 2006, 2012) and the inﬁnitesimal jackknife method for estimating the variance (Wager et al., 2014)—provide software implementations that are built on top of the randomForest R package (Liaw and…”
Random Forests for dependent data (2020, methodology): “Generalizations of the loss function used in RF have been shown to be successful in many different contexts like quantile estimation [47], distributional forests [61], causal forests [4], manifold forests emulating convolutional neural networks [55], and others [40, 73, 14, 58, 68].”
Random Forest Spatial Interpolation (2020, methodology): “In case of RF, prediction uncertainties were quantiﬁed using Quantile Regression Forest (QRF) [47].”
A random forest guided tour (2015, background): “Meinshausen (2006) shows that random forests provide information about the full conditional distribution of the response variable, and thus can be used for quantile estimation.”
Generalized additive models for location, scale and shape for high dimensional data—a flexible approach based on boosting (2012, neutral): “X/] (Meinshausen, 2006).”
A global map of mangrove forest soil carbon at 30 m spatial resolution (2018, methodology): “Finally, prediction error of OCS for 0–1 m depth was derived for±1 s.d. based on the quantile regression approach of Meinshausen (2006) and implemented in R via the ranger package.”
Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife (2013, background): “Several variants of random forests have been analyzed theoretically by, e.g., Biau et al. (2008), Biau (2012), Lin and Jeon (2006), and Meinshausen (2006).”
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests (2014, background): “The author considers only an idealized class of trees based on the assumptions in Meinshausen (2006) as well as additional honesty and regularity conditions that allow k n to grow at a faster rate, and demonstrates that when many Monte Carlo samples are employed, the inﬁnitesimal jackknife…”