Bootstrap Methods for Developing Predictive Models

By Peter C. Austin, J. Tu. 2004

620 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Statistics and Probability

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Citations in the corpus

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020, methodology): “Third, we use a parsimony plot (model performance vs complexity) to determine the appropriate number of variables ( m ), balancing the trade-off between performance and sparsity [39,40].”
Using semi-global matching point clouds to estimate growing stock at the plot and stand levels: application for a broadleaf-dominated forest in central Europe (2015, methodology): “In accordance with Austin and Tu (2004), the results from the bootstraps were summarized, and only explanatory variables with statistical significance ( = 0.”
Variable Selection Using Random Forests (2006, methodology): “Many automatic variable selection techniques have been proposed in the literature, for example the backward or forward stepwise regression (see Miller (1984) and Hocking (1976)) or the recent stepwise bootstrap method of Austin and Tu (2004).”
Prognosis and prognostic research: Developing a prognostic model (2009, background): “and exploring the stability (reproducibility) of a model.”
Tropospheric Rossby Wave Breaking and the NAO/NAM (2008, methodology): “For bootstrapping, we use the Akaike information criterion (AIC) and determine which predictors were retained in at least 60% of the trials—a method demonstrated to produce parsimonious models with excellent predictive capability (Austin and Tu 2004).”
Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. (2004, methodology): “In a related article, the authors [22] examine the use of backward variable elimination in conjunction with bootstrap methods to develop predictive models.”