Bootstrap tilting conndence intervals could be the method of choice in many applications for reasons of both speed and accuracy. With the right implementation , tilting intervals are 37 times as fast as bootstrap BC-a limits, in terms of the number of bootstrap samples needed for comparable simulation accuracy. Thus 100 bootstrap samples might suuce instead of 3700. Tilting limits have other de...

PURPOSE To develop a bootstrap method to assess the quality of High Angular Resolution Diffusion Imaging (HARDI) data using Q-Ball imaging (QBI) reconstruction. MATERIALS AND METHODS HARDI data were re-shuffled using regular bootstrap with jackknife sampling. For each bootstrap dataset, the diffusion orientation distribution function (ODF) was estimated voxel-wise using QBI reconstruction bas...

Propensity-score matching is frequently used to estimate the effect of treatments, exposures, and interventions when using observational data. An important issue when using propensity-score matching is how to estimate the standard error of the estimated treatment effect. Accurate variance estimation permits construction of confidence intervals that have the advertised coverage rates and tests o...

Degenerate U and V -statistics play an important role in the field of hypothesis testing since numerous test statistics can be formulated in terms of these quantities. Therefore, consistent bootstrap methods for U and V -statistics can be applied in order to approximate critical values of those tests. First, we prove a new asymptotic result for degenerate U and V -statistics of weakly dependent...

In this paper we review recent developments on a bootstrap method for robust estimators which is computationally faster and more resistant to outliers than the classical bootstrap. This fast and robust bootstrap method is, under reasonable regularity conditions, asymptotically consistent. We describe the method in general and then consider its application to perform inference based on robust es...

In this paper we consider general rst order autoregression, including the stationary, the explosive and the unstable case. It is well-known in the literature that the usual bootstrap method for the least squares parameter estimator is asymptotically consistent for the stationary and the explosive case, but does not work in the unstable case, where the parameter value is equal to + 1 or {1. We p...

The bootstrap statistical method is applied to the discrepancy in the l-charged particle decay modes of the tau lepton. This eliminates questions about the correctness of the errors ascribed to the branching fraction measurements and the use of gaussian error distributions for systematic errors. The discrepancy is still seen when the results of the bootstrap analysis are combined with other mea...

This paper establishes the higher-order equivalence of the k-step bootstrap, introduced recently by Davidson and MacKinnon (1999), and the standard bootstrap. The k-step bootstrap is a very attractive alternative computationally to the standard bootstrap for statistics based on nonlinear extremum estimators, such as generalized method of moment and maximum likelihood estimators. The paper also ...

1 This paper establishes the higher-order equivalence of the k-step bootstrap, introduced recently by Davidson and MacKinnon (1999), and the standard bootstrap. The k-step bootstrap is a very attractive alternative computationally to the standard bootstrap for statistics based on nonlinear extremum estimators, such as generalized method of moment and maximum likelihood estimators. The paper als...

Bootstrap principle is briefly reviewed. Hall’s (1989) antithetic variates method for bootstrap is discussed and extended to more than two antithetic resampling processes. We illustrate the theory with a simulation study. The numerical results show that increasing the number of antithetic resampling processes produces significant smaller variances of the bootstrap estimator over the paired case.