In this paper we investigate to what extent the bootstrap can be applied conditional mean models, such as regression or time series when volatility of innovations is random and possibly non-stationary. fact, many economic financial displays persistent changes possible non-stationarity. However, theory for models has focused on deterministic unconditional variance little known about performance ...

The smooth bootstrap for estimating copula functionals in small samples is investigated. It can be used both to gauge the distribution of estimator question and augment data. Issues arising from kernel density estimation domain are addressed, such as how avoid bounded domain, which bandwidth matrix choose, smoothing carried out. Furthermore, we investigate impacts underlying dependence structur...

BACKGROUND Statistical inference of signals is key to understand fundamental processes in the neurosciences. It is essential to distinguish true from random effects. To this end, statistical concepts of confidence intervals, significance levels and hypothesis tests are employed. Bootstrap-based approaches complement the analytical approaches, replacing the latter whenever these are not possible...