In this paper, we propose new asymptotic confidence intervals for extreme quantiles, that is, quantiles located outside the range of available data. We restrict ourselves to situation where underlying distribution is heavy-tailed. While are mostly constructed around a pivotal quantity, consider here an alternative approach based on order statistics sampled from uniform distribution. The converg...

In this paper, a new two-sampling scheme is proposed to construct appropriate confidence intervals for the lower population quantiles. The confidence intervals are determined in the parametric and nonparametric set up and the optimality problem is discussed in each case. Finally, the proposed procedure is illustrated via a real data set. 

Bootstrap Effect Sizes (bootES; Gerlanc & Kirby, 2012) is a free, open source software package for R (R Development Core Team, 2012), which is a language and environment for statistical computing. BootES computes both unstandardized and standardized effect sizes (such as Cohen’s d, Hedges’s g, and Pearson’s r), and makes easily available for the first time the computation of their bootstrap CIs...

We construct bootstrap confidence intervals for smoothing spline and smoothing spline ANOVA estimates based on Gaussian data, and penalized likelihood smoothing spline estimates based on data from exponential families. Several variations of bootstrap confidence intervals are considered and compared. We find that the commonly used bootstrap percentile intervals are inferior to the T intervals an...

We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods. Our work builds on variance estimates for bagging proposed by Efron (1992, 2012) that are based on the jackknife and the infinitesimal jackknife (IJ). In practice, bagged predictors are computed using a finite number B of bootstrap replicates, and worki...