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(The lack of a pivotal quantity also makes it hard to construct exchangeable quantities suitable for the more usual kinds of bootstrap.) …

The term "bootstrap" covers many (somewhat related) things -- enough to fill a number of books (which indeed it does). Some things are more prone to problems with naive application than others. … If you have a model for the way the expectation moves, the way the variance changes and the dependence over time, you might be able to do something like block-bootstrap the residuals, say. …

This is related to collision-counting in the birthday problem. Imagine you walk into a room of $k$ people. The probability at least one shares a birthday with you is $q(k;n) = 1 - \left( \frac{n-1}{ …

In a great many cases, however, asymptotic and bootstrap P values are quite different. … In this paper, we discuss techniques for computing modified bootstrap P values which will tend to be similar to the ordinary bootstrap P value when the latter is reliable, but which should often …

Immediately, we have the result at the top of this answer, $\lim_{n\to\infty}(1- 1/n)^n=e^{-1}$ As gung points out in comments, the result in your question is the origin of the 632 bootstrap rule … Tibshirani (1997), "Improvements on Cross-Validation: The .632+ Bootstrap Method," Journal of the American Statistical Association Vol. 92, No. 438. (Jun), pp. 548-560 …

Because we cannot do that, we use bootstrap. We bootstrap using something close to the true distribution. But which kind of closeness is more important in a particular simulation? …

If you compare a hypothesized value with a bootstrapped confidence interval for a parameter, you'd be comparing a hypothesized value to a confidence interval based on an empirical distribution from bo …

So, how is the new sample different from the original one? I don't have a huge background in statistic so please be plain. Sampling is with replacement. Imagine you have a sample of size 30. Tak …

Sometimes you will see two used when the bootstrap samples are in turn resampled. Since there are many bootstrap samples, you need a way to indicate which one you're referring to. … For example, if you want to write how to compute some statistic across samples, you need some way to indicate the $k$-th bootstrap sample (say). …

You're using the bootstrap to try to get at the sampling distribution of the sample mean, by using the resampling distribution to approximate it. …

It won't work with raw residuals for several reasons, but arguably can work - at least for some cases - with other choices. E.g pearson residuals can take care of the heteroskedasticity issue. Other c …

Note that the overlapping block bootstrap is not the only block-bootstrap method. My comments should apply to the other forms as well. …

observations* (the original data has more digits): x y 1 1.66 3.64 2 5.30 4.91 3 4.75 5.32 4 2.07 1.58 5 2.88 4.25 6 3.53 4.59 7 1.75 2.37 8 1.42 2.10 9 2.82 4.35 10 1.81 3.90 and I want to bootstrap … *(ten is much too few for the bootstrap to be much use, but this is just for illustration) The idea is to resample the data - by sampling rows (with replacement) from the original data to obtain new pseudo-samples …

The bootstrap (alone) will not help you identify the mean in the presence of censoring, and will not be much use with the median given a dataset like the one you supplied; you may be able to get some kind … of lower bound on the median in this case with a bootstrap, but you can do that nonparametrically without the need for bootstrapping. …

What varies is the number of such bootstrap resamples. … Increasing the number of bootstrap samples gives a more "accurate" sense of that approximation, but it doesn't add any information that wasn't already there. …