ON THE CHOICE OF m IN THE m OUT OF n BOOTSTRAP AND CONFIDENCE BOUNDS FOR EXTREMA

By Peter J. Bickel, A. Sakov. 2008

198 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3112e70f57973a443f3ba9d348c6daebac45eff0

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Citations in the corpus

The Big Data Bootstrap (2012, background): “While schemes have been proposed for automatic selection of an optimal value of m (Bickel & Sakov, 2008), they require significantly greater computation which would eliminate any computational gains.”
A scalable bootstrap for massive data (2011, methodology): “One could consider similarly increasing b from some small value until the output of BLB stabilizes (an approach reminiscent of the method proposed in Bickel and Sakov (2008) for the m out of n bootstrap); devising a means of doing so eﬃciently is the subject of future work.”
Tail index estimation: quantile driven threshold selection (2016, methodology): “This procedure is partially inspired by Bickel and Sakov (2008). Bickel and Sakov (2008) consider a bootstrap procedure for inference regarding the tail of a distribution.”
Inference by the m out of n bootstrap in nonparametric frontier models (2011, methodology): “Recent papers by Politis et al. (2001) and Bickel and Sakov (2008) provide theoretical results and practical suggestions for choosing the size of subsamples when using subsampling for inference.”
Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods (2019, methodology): “Bickel and Sakov (2008) propose a data-driven method to select m.”
Matching and semi-parametric IV estimation, a distance-based measure of migration, and the wages of young men (2011, methodology): “Since it is unknown whether the standard n -bootstrap is appropriate for the matching estimators that we consider, we use an algorithm from Bickel and Sakov (2008) to implement the ‘m out of n’ bootstrap procedure.”
A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data (2013, methodology): “Regarding the relationship between RSA and bootstrap, we note that they are closely related: Equation (7) is the m out of n without replacement bootstrap estimator (e.g., Politis et al. 2001; Bickel and Sakov 2008) of the partial derivative of the Kullback–Leibler divergence defined in (6).”
Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives (2013, background): “Both Bickel and Sakov (2008) and Politis et al. (2001) proposed computing the object of interest (e.”
Random Forests for Big Data (2015, methodology): “of m and the data-driven scheme proposed in [30] for the selection of m requires to test several different values of m and eliminates computational gains.”