Differentiable Functionals and Smoothed Bootstrap

Smoothed Bootstrap and Jackboot Sampling Smoothed Bootstrap and Jackboot Sampling

We propose a bootstrap sampling method jackboot sampling This provides more accu rate inferences than ordinary bootstrap sampling better con dence interval coverage and less biased or unbiased standard errors The method is simple to implement We also prescribe a smoothing parameter for use in smoothed bootstrapping using or dinary kernel smoothing The e ect is similar to that of jackboot sampling

On Differentiable Functionals

An Infinity Differentiable Weight Function for Smoothed Article Hydrodynamics Approaches

In this paper, we make a brief review of smoothed particle hydrodynamic method and develop a new infinity differentiable weight function in the three-dimensional space. The function satisfies the demanded properties. Furthermore, we investigate the consistency and analyze the estimation errors of the SPH interpolation with the weight function.

Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation

This paper is concerned with the estimation of a classifier’s accuracy. We present a number of novel bootstrap estimators, based on kernel smoothing, that consistently show superior performance on both synthetic and real data, with respect to other established methods. We call the process of (re)sampling the data via kernel-based smoothed bootstrap data cloning. The new cloning methods outperfo...

Iterated Smoothed Bootstrap Confidence Intervals for Population Quantiles

This paper investigates the effects of smoothed bootstrap iterations on coverage probabilities of smoothed bootstrap and bootstrap-t confidence intervals for population quantiles, and establishes the optimal kernel bandwidths at various stages of the smoothing procedures. The conventional smoothed bootstrap and bootstrap-t methods have been known to yield one-sided coverage errors of orders O(n...