Bootstrap Methods: A Guide for Practitioners and Researchers

By Michael R. Chernick. 2007

945 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/de1f9f0ab871dffdab80f5c8ba16188e5194b243

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In Control and Systems Engineering

In Management Science and Operations Research

Citations in the corpus

Distributionally Robust Convex Optimization (2014, neutral): “Confidence regions for the other indicators discussed in this section can be constructed from historical observations using resampling techniques such as jackknifing or bootstrapping [24].”
An investigation of the cross-national determinants of customer satisfaction (2011, background): “…207 small (overall, but especially the sub-samples used in the sensitivity analyses) and thus more susceptible to influential observations that could skew the analysis, bootstrapping will help gauge the sensitivity of our estimates to sampling variations (Chernick 2008; Efron and Tibshirani 1986).”
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration (2012, methodology): “For internal validation, the specific validation algorithm used should be described (for example, bootstrapping, 10-fold or leave-one-out cross-validation) [147-149].”
A Tutorial on Regularized Partial Correlation Networks (2016, methodology): “After estimating a network, bootstrapping methods (Chernick, 2011; Efron, 1979) can be used to gain insight into the accuracy and stability of the network parameters and descriptive statistics based on the estimated network structure (e.g., centrality indices).”
Advantages of Monte Carlo Confidence Intervals for Indirect Effects (2012, background): “Chernick (2008) defines a 100(1 − α)% confidence interval (CI) for the value of θ in the following way: “.”
When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA (2020, background): “However, highly skewed data may inflate (bootstrap) standard errors and reduce statistical power (Chernick, 2008), which is why, at the very least, it is advisable to report information on the data distribution (Hair et al., 2012b).”
Bootstrap based confidence limits in principal component analysis: a case study (2013, methodology): “The bootstrap estimates standard errors of statistics from the empirical (original) data by re-sampling [3–7].”
Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects (2010, methodology): “observed data, with replacement, calculating the indirect effect on each sample— here the product of the unstandardized regression coefficients from Equations (1) and (2)—and forming a CI based on the distribution of the indirect effect across all samples (see Chernick, 2007, and Efron & Tibshirani,”
Understanding the Size Selectivity of Redfish (Sebastes spp.) in North Atlantic Trawl Codends (2012, background): “This innovation enabled the estimation of the “Efron percentile” 95% confidence limits (Efron, 1982; Chernick, 2007) for the entire average selection curve over hauls. One advantage of this approach is the possibility to estimate confidence bands for the curve without having to rely on the “delta th”