Bootstrapping is a nonparametric approach for evaluating the distribution of a statistic based on random resampling. This article illustrates the bootstrap as an alternative method for estimating the standard errors when the theoretical calculation is complicated or not available in the current software.

Computer-Intensive methods for estimation assessment provide valuable information concerning the adequacy of applied probabilistic models. The bootstrap method is an extensive computational approach to uncertainty estimation based on resampling and statistical estimation. It is a powerful tool, especially when only a small data set is used to predict the behaviour of systems or processes. This ...

The bootstrap methodology, initially proposed in independent situations, has revealed inefficient in the context of dependent data. Here, the estimation of population characteristics is more complex. This is what happens in the context of time series. There has been a great development in the area of resampling methods for dependent data. A revision of different approaches of this methodology f...

——————————————————————————————————— It is common in parametric bootstrap to select the model from the data, and then treat it as it were the true model. Kilian (1998) have shown that ignoring the model uncertainty may seriously undermine the coverage accuracy of bootstrap confidence intervals for impulse response estimates which are closely related with multi-step-ahead prediction intervals. In...

BACKGROUND A two-arm non-inferiority trial without a placebo is usually adopted to demonstrate that an experimental treatment is not worse than a reference treatment by a small pre-specified non-inferiority margin due to ethical concerns. Selection of the non-inferiority margin and establishment of assay sensitivity are two major issues in the design, analysis and interpretation for two-arm non...

Resampling techniques are used widely within the ERP community to assess statistical significance and especially in the deception detection literature. Here, we argue that because of statistical bias, bootstrap should not be used in combination with methods like peak-to-peak. Instead, permutation tests provide a more appropriate alternative.

Many machine learning models have important structural tuning parameters that cannot be directly estimated from the data. The common tactic for setting these parameters is to use resampling methods, such as cross–validation or the bootstrap, to evaluate a candidate set of values and choose the best based on some pre–defined criterion. Unfortunately, this process can be time consuming. However, ...

Resampling significance tests possess a number of desirable properties: they are conceptually simple, unbiased, powerful and free of assumptions about the parent populations involved. There exists a huge literature in this area, including canonical works by Fisher [1], Pitman [2], Efron [3], Edgington [4], Davison and Hinkley [5] and Good [6]. Unfortunately, even if we restrict ourselves to the...

When fitting predictive survival models to high-dimensional data, an adequate criterion for selecting model complexity is needed to avoid overfitting. The complexity parameter is typically selected by the predictive partial log-likelihood (PLL) estimated via cross-validation. As an alternative criterion, we propose a relative version of the integrated prediction error curve (IPEC), which can be...

Theory in time series analysis is often developed in the context of nite-dimensional models for the data generating process. Whereas corresponding estimators such as those of a conditional mean function are reasonable even if the true dependence mechanism is of a more complex structure, it is usually necessary to capture the whole dependence structure asymptotically for the bootstrap to be vali...