Bootstrap Methods and Their Application

By S. T. Buckland, A. C. Davison, D. V. Hinkley. 1998

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Citations in the corpus

Kernel‐based tests for joint independence (2016, methodology): “Further details including critical values are provided in the on-line appendix B. Davison and Hinkley (1997) suggested the use of B between 99 and 999.”
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (2016, background): “Other literature has suggested that it should be chosen to be approximately κ = 2000 (Efron & Tibshirani, 1993; Davison & Hinkley, 1997).”
ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation (2004, methodology): “95% confidence intervals are estimated using bootstrap resampling (Davison and Hinkley 1997).”
Document Context Language Models (2015, methodology): “To give a robust model comparison with the limited number of documents available in the PTB test set, we employ bootstrapping (Davison & Hinkley, 1997).”
Fermi 130 GeV gamma-ray excess and dark matter annihilation in sub-haloes and in the Galactic centre (2012, methodology): “This can be obtained by smoothed bootstrap [19, 20].”
Accuracy assessment of digital elevation models by means of robust statistical methods (2009, methodology): “Using the bootstrap is one approach to assess the uncertainty of the above sample quantiles as estimators of the true quantiles of the underlying distribution (Davison and Hinkley, 1997). Here one draws a sample of size n with replacement from the available data {x1, . . . , xn} and then uses this n”
Goodness‐of‐fit tests for high dimensional linear models (2015, result): “This result is a straightforward consequence of the fact that under the null hypothesis {Q̃b}Bb=0 form an exchangeable sequence, and standard results on Monte Carlo testing (see Davison and Hinkley (1997), chapter 4, for example).”
Asymmetrical responses of forest and “beyond edge” arthropod communities across a forest–grassland ecotone (2014, unknown): “…and Didham’s (2006) recommendations we used the first and second derivatives of the functions and a bootstrap technique of resampling of the errors (Davison and Hinkley 1997; Toms and Lesperance 2003) to objectively estimate the underlying parameters of edge effect and their confidence intervals…”
A pairwise likelihood approach to generalized linear models with crossed random effects (2005, methodology): “Hence, we have either to choose a suitable scale (parameterization) for the variance component, or to use parameterization invariant methods, like the bootstrap percentile method for confidence intervals, see Davison and Hinkley (1997: section 5.3). The second point concerns testing of null variance”
New estimates of the UK real and nominal yield curves (2001, methodology): “To estimate the overall out-of-sample goodness of fit we leave out each bond in turn, estimate the yield curve, and calculate the average fitting error of the omitted bonds, a procedure known as cross-validation (Davison and Hinkley (1997)). be naturally divided in the same way.”
The vSLAM Algorithm for Robust Localization and Mapping (2005, methodology): “By also using a localization scheme with a particle filter [2] and an adaptive mixed proposal distribution [3], [5], vSLAM offers a breakthrough”
Variance Bounds on the Permanent and Transitory Components of Stochastic Discount Factors (2012, methodology): “Following Davison and Hinkley (1997), the 90% bootstrap confidence interval for the variance bound v is obtained as [ 2v−q(1− a 2 ) , 2v−q( a 2 )] , where a = 0.”