The nonparametric bootstrap approach is known to be suitable for calculating central confidence intervals for the locations of quantitative trait loci (QTL). However, the distribution of the bootstrap QTL position estimates along the chromosome is peaked at the positions of the markers and is not tailed equally. This results in conservativeness and large width of the confidence intervals. In th...

In this paper we study a bootstrap strategy for estimating the variance of a mean taken over large multifactor crossed random effects data sets. We apply bootstrap reweighting independently to the levels of each factor, giving each observation the product of its factor weights. No exact bootstrap exists for this problem (McCullagh, 2000). We show that the proposed bootstrap is mildly conservati...

The .632 error estimator is a bias correction of the bootstrap estimator which leads to an underestimation of the error when the apparent error is zero. As a consequence Efron and Tibshirani (1997) developed the .632+ bootstrap error as a modification that can handle this case. We demonstrate properties and behavior of this error estimation technique. Furthermore, we show how to apply the boots...

For right censored data, Efron (1981) has shown that his “simple” and “obvious” methods of bootstrapping are equivalent. We explain why this equivalence no longer holds for truncated data. Wang (1991) generalized Efron’s “obvious” bootstrap method to data that are both left truncated and right censored, under the assumption that C ≥ T and C − T is independent of T , where T and C denote the (ge...

Estimation of Kullback-Leibler information is a crucial part of deriving a statistical model selection procedure which, like AIC, is based on the likelihood principle. To discriminate between nested models, we have to estimate KullbackLeibler information up to the order of a constant, while Kullback-Leibler information itself is of the order of the number of observations. A correction term empl...

Evolutionary trees are often estimated from DNA or RNA sequence data. How much confidence should we have in the estimated trees? In 1985, Felsenstein [Felsenstein, J. (1985) Evolution 39, 783-791] suggested the use of the bootstrap to answer this question. Felsenstein's method, which in concept is a straightforward application of the bootstrap, is widely used, but has been criticized as biased ...

This article describes how, in the high-level software packages used by non-statisticians, approximate non-parametric bootstrap samples can be created and analyzed without physically creating new data sets, or resorting to complex programming. The comparable performance of this shortcut method, which uses Poisson rather than multinomial frequencies for the numbers of copies of each observation,...

A study on using non-parametric statistical methods was carried out to calculate the binding constant of an inclusion complex and to estimate its associated uncertainty. First, a correct evaluation of the stoichiometry was carried out in order to ensure an accurate determination of the binding constant. For this purpose, the modified Benesi-Hildelbrand method had been previously applied. Then, ...

The block bootstrap is the best known bootstrap method for time-series data when the analyst does not have a parametric model that reduces the data generation process to simple random sampling. However, the errors made by the block bootstrap converge to zero only slightly faster than those made by first-order asymptotic approximations. This paper describes a bootstrap procedure for data that ar...

The -level Conditional Tail Expectation (CTE) of a continuous random variable X is defined as its conditional expectation given the event {X q }, where q represents its -level quantile. It is well known that the empirical CTE (the average of the n(1 ) largest order statistics in a sample of size n) is a negatively biased estimator of the CTE. This bias vanishes as the sample size increases but ...