In this paper we propose a new bootstrap, or Monte-Carlo, approach to such problems. Traditional bootstrap methods in this context are based on fitting a process chosen from a wide but relatively conventional range of discrete time series models, including autoregressions, moving averages,...

The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In...

Histogram-based entropy estimation is well known for its computational efficiency and using bootstrap method to estimate its bias is widely accepted. However, when we apply it to entropy estimator constructed by "plug-in" method, we have to use histogram to estimate its pdf at first. Thus, in...

In risk management areas such as reinsurance, the need often arises to construct a confidence interval for a quantile in the tail of the distribution; for example, there is high probability that the sample maximum lies near or below the quantile. While different methods, including subsample...