The bootstrap: a tutorial

By Ron Wehrens, Hein Putter, Lutgarde M. C. Buydens. 2000

585 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/158bd50ec6a0caee2c17593cfcae6ce9d5d1992b

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In Analytical Chemistry

In Statistics and Probability

Citations in the corpus

On further application of r m2 as a metric for validation of QSAR models (2009, methodology): “The internal validation tools include leave-one out (LOO) and leave-many-out (LMO) cross-validation [19,20], bootstrapping [21] and Y-randomization [19].”
Preventing young farmers from leaving the farm: Investigating the effectiveness of the young farmer payment using a behavioural approach (2019, methodology): “4, the t values (estimated by means of the bootstrap method, see Varian, 2005) of the regression coefficients between the constructs or latent variables are highly significant at the 95% significant level.”
Variable selection in regression—a tutorial (2010, methodology): “where n is the number of jack-knife models, p(i) is the estimated parameter and p is the mean of the estimated parameter from all the calculated models [20].”
Fitting Straight Lines with Replicated Observations by Linear Regression: The Least Squares Postulates (2004, unknown): “On the other hand computer intensive methods of inference, for example, bootstrap ( 94–97 ) or jacknife ( 98–99 ), are recognized as powerful tools in judging data in complicated, nonstandard situations with only a few distributional assumptions.”
Bootstrap based confidence limits in principal component analysis: a case study (2013, methodology): “Sufficiently large B decreases the Monte Carlo error of the re-sampling (the difference in the outcome when the complete procedure of uncertainty estimation is repeated), which therefore increases the accuracy of the bootstrap approximation [5,7].”
Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. (2003, unknown): “Because there is only one data set, bootstrapping simulates what would happen if the population were resampled by randomly resampling the data set (Efron and Tibshirani 1993; Wehrens et al. 2000).”