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What is one limitation of using a bootstrap sample?
It does not treat the original sample as if it is the population even those it involves sampling with replacement from the original sample. It assumes that sampling with replacement from the original sample of size n mimics taking a sample of size n from a larger population.
What is the disadvantage of bootstrap?
The Disadvantages of Bootstrap are: Styles are verbose and can lead to lots of output in HTML which is not needed. JavaScript is tied to jQuery and is one of the commonest library which thus leaves most of the plugins unused.
Can a bootstrap be used in a small sample?
“The theory of the bootstrap involves showing consistency of the estimate. So it can be shown in theory that it works in large samples. But it can also work in small samples. I have seen it work for classification error rate estimation particularly well in small sample sizes such as 20 for bivariate data.
Which is an example of a problem with bootstrap?
(1) Issues with resampling. One of the problems with bootstrap, either for small or large samples, is the resampling step. It is not always possible to resample while keeping the structure (dependence, temporal.) of the sample. An example of this is a superposed process.
Which is the sampling distribution assumed in Bootstrap?
The basic nonparametric bootstrap assumes that the sample is taken at random from a population. So for any sample size n the distribution for samples chosen at random is the sampling distribution assumed in bootstrapping.
Are there any problems with nonparametric bootstrap?
In case you really want to find issues of using nonparametric bootstrap, here are two problems: (1) Issues with resampling. One of the problems with bootstrap, either for small or large samples, is the resampling step. It is not always possible to resample while keeping the structure (dependence, temporal.) of the sample.
https://www.youtube.com/watch?v=9STZ7MxkNVg