Search Results

You do not compare means of simple bootstrap resamples to the sample mean. That doesn't test anything. …

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 …

Can Bootstrap Resampling be used to Calculate a Confidence Interval for the Variance of a Data Set? Yes, just as with many other statistics. … It is not always the case that if you bootstrap a mean the bootstrap means will follow a normal distribution, even for distributions for which the CLT applies. …

If the sampling is without replacement, it's a permutation test; if it's with-replacement, then it would be a bootstrap test. …

You can do it by progressing conditionally through the categories. I'm going to work from the last category backward (for a particular reason) but it can be done in any order as long as you're consist …

With the null hypothesis being that the means differ by some fixed constant, and against a two sided alternative. There's a couple if ways to approach this. Here's one (which is indeed as you suggest, …

Resampling methods like bootstrap and permutation tests give ways to make nonparametric tests. You can make them for all manner of situations. … If you want to do a bootstrap test of a coefficient in some complicated model, you can do that. …

The conflating of testing for a difference in means with testing for a difference in distributions is, unfortunately, misleading. It's important to focus on the alternatives you're trying to find. Eve …

intervals) from the outset, and instead focuses on trying to get reasonably good coverage in large samples (sometimes with less success than may be understood; if you haven't checked, don't assume your bootstrap … Permutation tests can work on small samples (though limited choice of significance levels can sometimes be a problem with very small samples), while the bootstrap is a large-sample technique (if you use …

(Davison & Hinkley describe better but more complicated one sample bootstrap test than this - e.g. the studentized bootstrap in Section 4.4.1 of their book. … of the boot package Davison and Kuonen on bootstrap applications in R …

It doesn't matter how large a sample size you choose, there's always distributions for which that sample size is not sufficient to make sample means look close to normal, even though the CLT holds for …

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. …

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 …

The idea is to emulate the sampling distribution under the null hypothesis (from which you get an approximate p-value). So you make a sample that's shaped like the one you have but with a mean like t …

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. …