bootstrapping
- noun
- /ˈbuːtstræpɪŋ/
- Specialized
- The accuracy of the final model was validated through bootstrapping, which involved resampling the dataset multiple times.
- use of bootstrapping
- bootstrapping with 1,000
Examples
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Bootstrapping with 1,000 replicates was performed to determine the percentage reliability for each internal node.
Academic text (2014) -
The bootstrapping technique for the population mean with the uniform (0, 1) population is illustrated below.
Academic text (1990) -
Associations with heat wave days defined using different HIs were compared by bootstrapping.
Academic text (2014) -
Bootstrapping was used to account for these missing clusters. This analysis was conducted with all participants.
Academic text (2012) -
Bootstrapping can be time-consuming in more complex models because of the requirement to repeatedly retest the model.
Academic text (2010) -
All analyses were repeated with the use of bootstrapping to determine whether conclusions generalized across traditional methods of analysis versus robust methods of analysis; the conclusions were the same.
Academic text (2010) -
In the 1995-1997 survey, a small number of clusters were not sampled in the second year of the survey for budgetary reasons; bootstrapping was used to account for unsampled clusters.
Academic text (2012) -
We did not seek external replication because of the multitude of specific risk factors and instead relied on bootstrapping and cross-validation to guide the LASSO analysis and the estimation of the C-index.
Academic text (2012) -
Several studies have identified the presence of bidirectional bootstrapping between lexical and grammatical development at ages 2 and 3 years (Dionne et al., 2003; Moyle et al., 2007).
Academic text (2012) -
The bootstrapping method allowed the researchers to generate confidence intervals for their estimates.
Synonyms
Taking repeated samples from data to check how much the results can vary
Surface Forms
Morphology
Etymology
Bootstrapping comes from the image of pulling yourself up by your bootstrap, a phrase meaning 'to pull yourself up'. In statistics, bootstrapping means making many new samples from your own data to check how stable an estimate is, like testing your result by using only what you already have.