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We also employ a parametric bootstrap approach to estimate the mean squared prediction error (MSPE) of the PEB predictor.
The use of more multiple observations per country is taken into account by using a bootstrap approach to estimate the confidence intervals.
A natural question when employing the bootstrap approach to estimate confidence intervals concerns the number of bootstrap samples needed to achieve accurate intervals.
The similarity in performance between the bootstrap estimate of performance on the training set and performance on a sequestered validation dataset validates use of the bootstrap approach to estimate model performance.
This method uses a leave-pair-out (LPO) bootstrap approach to estimate the mean AUC (mean over training sets) and a "delta method after bootstrap" to estimate the variability of the estimated mean AUC.
Because the human EGP data set consists of a relatively small number of SNPs, many of which lie close to one another, we have tried to minimize the effects of linkage in our bootstrap approach to estimate parameters and confidence intervals.
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In this study, we used a subsampling bootstrap approach to estimating maximum likelihood estimates for common national effects for associations of individual pollutants with mortality.
We used a maximum-likelihood approach to infer the model parameters and a nonparametric bootstrap resampling approach to estimate confidence intervals and confidence regions.
Due to the fact that the latter data are only available in simulation studies, but not for real biological data, a bootstrap ensemble is a valid approach to estimate the variability of the population of inferred networks from an ensemble of data sets.
PRUNIER uses a more conservative approach to estimate lateral transfer events, since it also includes a bootstrap cutoff.
We have proposed a maximum likelihood approach to estimate kinetic parameters using nonlinear optimization, with estimates on the standard error and bias of the results using the bootstrap.
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