Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy

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Trend analysis of climate time series: A review of methods (2019, neutral): “Lahiri (2003) presents theoretical comparisons between the MBB, the SB, the autoregressive boostrap (ARB), and other bootstrap variants.”
A Matched-Filter Analysis of the Tidal Tails of the Globular Cluster Palomar 5 (2002, methodology): “Standard bootstrap techniques (Efron 1982; Efron & Tibshirani 1986) were used to find the variance for the measurement of each subsample, and this variance is plotted versus subsample size in Figure 12.”
Meta-analysis of the effects of undersown catch crops on nitrogen leaching loss and grain yields in the Nordic countries (2015, methodology): “We used a bootstrap statistical method (Efron and Tibshirani, 1986) to generate bias-corrected 95% confidence intervals (CIs) around the log response ratios from 4999 iterations.”
Drought-driven change in wildlife distribution and numbers: a case study of koalas in south west Queensland (2011, methodology): “To obtain a measure of the accuracy of our parameter estimates, we used a bootstrap method to estimate a standard error of koala abundance (Efron and Tibshirani 1986; Sullivan et al. 2004).”
The estimating function bootstrap (2000, background): “The most studied problem in the bootstrap literature is that of determining reliable confidence limits; see, for examples Efron (1987), Efron & Tibshirani (1986), DiCiccio & Romano (1988) and DiCiccio & Efron (1996).”