The Jackknife Estimate of Variance

By Bradley Efron, C. Stein. 1981

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In Statistics and Probability

Citations in the corpus

Marginal Release Under Local Differential Privacy (2017, methodology): “One such approach is the Efron-Stein decomposition [12] which is a generalization of Hadamard transform for non binary contingency tables.”
Local Linear Forests (2018, methodology): “Subsampling plays a central role in our asymptotic theory, allowing us to prove asymptotic normality by building on the work of Efron and Stein (1981). Moreover, subsampling is what we use to tune the bias-variance trade-off of the forest: Forests whose trees are grown on small subsamples have lower”
Generalized random forests (2016, methodology): “…2.4), θ̃∗(x) is an infinite-order U -statistic whose order corresponds to the subsample size, and so the arguments of Mentch and Hooker (2016) or Wager and Athey (2018) can be used to study the averaged estimator θ̃∗(x) using results about U -statistics (Hoeffding, 1948; Efron and Stein, 1981).”
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, background): “, Hastie et al. [2009]. In the context of causal trees, we analogously want to think of the leaves as small enough that the (Yi, Wi) pairs corresponding to the indices i for which i ∈ L(x) act as though they had come from a randomized experiment.”
Measuring Association on Topological Spaces Using Kernels and Geometric Graphs (2020, background): “The special case q = 2 yields the Efron-Stein inequality (see [35]).”
Stein's Method and Stochastic Analysis of Rademacher Functionals (2008, background): “A connection between jackknife and Hoeffding decompositions is explored for example in [13].”
The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations (2003, methodology): “Precision was estimated by jackknifing over a typical sampling trip (30 days) (Efron and Stein 1981).”
Random geometric complexes in the thermodynamic regime (2014, background): “[12] and for the case of random vectors, [37, (2.”
Gaussian Bounds for Noise Correlation of Functions (2007, background): “It is well known that the Efron-Stein decomposition exists and that it is unique [ 8 ].”
More Places than Crimes: Implications for Evaluating the Law of Crime Concentration at Place (2017, background): “plain random sampling), the jackknife re-sampling approach (Efron and Stein 1981) seems to best fulfill the need of most studies of crime concentration.”
Global sensitivity analysis for repeated measures studies with informative drop‐out: A semi‐parametric approach (2018, background): “This estimator is known to be conservative (Efron and Stein, 1981).”
Lower bounds for covering times for reversible Markov chains and random walks on graphs (1989, background): “149 4.6.3 Efron-Stein inequalities . . . . . . . . . . . . . . . .”
Modeling distribution and abundance of Antarctic baleen whales using ships of opportunity (2006, methodology): “Although the number of trips is small (14), each individual trip can be thought of as independent, and in the absence of a better alternative, the method we used to estimate variance was the jackknife (Miller 1974, Efron 1979, Efron and Stein 1981), using each trip as the resampling unit.”