The Jackknife: The Bootstrap and Other Resampling Plans.

By L. Low, Bradley Efron. 1983

Subfields: Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Local Linear Forests (2018, methodology): “Early versions of confidence intervals for random forests, backed by heuristic arguments and empirical evidence, were proposed by Sexton and Laake [2009] and Wager, Hastie, and Efron [2014]. Mentch and Hooker [2016] then established asymptotic normality of random forests where each tree depends on a”
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling (2018, methodology): “ (3) where we take the noise covariance EET to also be BBT and H = 2 N (BBT+ EET) 1. Bootstrap A simple empirical approach to approximate the sampling distribution of any estimator is the Bootstrap (Efron, 1982). The main idea is to simultaneously train qmodels, where each model i is based on a diff”
Uncertainty-Aware Reinforcement Learning for Collision Avoidance (2017, methodology): “hniques that go beyond direct discriminative training. In order to obtain accurate uncertainty estimates from our model, we use two techniques: bootstrapping and dropout. Bootstrapping: Bootstrapping [5, 6] is a simple and effective method of estimating model uncertainty using resampling that can be”
Consistency of Random Forests (2014, background): “lacement. Selecting the points “without replacement” instead of “with replacement” is harmless—in fact, it is just a means to avoid mathematical diﬃculties induced by the bootstrap; see, for example, Efron (1982), Politis, Romano and Wolf (1999). On the other hand, letting the parameters an and tn d”
Understanding Membership Inferences on Well-Generalized Learning Models (2018, methodology): “of reference records, the resulting datasets may share many records, and the reference models built from them would be alike and give similar outputs. To address this issue, we use bootstrap sampling [12] to generate the Figure 4: Generate new features for vulnerable record selection reference datas”
Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models (2006, methodology): “Resampling, or bootstrap, methods (Efron (1983)) have been successfully used for approximating the finite-sample null distributions of test statistics in a range of settings, both parametric and nonparametric.”
Nonparametric calibration for age estimation (2002, methodology): “Hence all the estimates were made by using a jackknife resampling strategy (Efron, 1982).”