Timeline for What is the purpose of using duplicated data in resampling techniques (e.g., bagging/bootstrapping)?
Current License: CC BY-SA 4.0
6 events
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| Sep 4, 2020 at 14:54 | comment | added | EdM | Having identical values doesn't make samples dependent. Think about rolling a die repeatedly: there are only 6 possible outcomes so you will get many values repeated as you roll over and over. Nevertheless, the distribution among those values can provide information about whether the die is fair or not. In bootstrapping, the data that you have are considered to be a discrete representation of the underlying population. Sampling without replacement then means that subsequent samples depend on what happened in prior samples. That's dependency in sampling even if all values are distinct. | |
| Sep 3, 2020 at 23:15 | comment | added | 24n8 | @EdM Just read your answer in that post. So essentially, the idea is if we're allowed to sample with replacement, as with bootstrapping, then each draw from the pool is independent? But then this seems to come with the cost that the samples within the bootstrapped dataset is not entirely independent. If you resampled without replacement, then your bootstrapped dataset samples would be independent (assuming the original pool of data was independently generated)? | |
| Sep 3, 2020 at 21:07 | comment | added | EdM | There's a similar question and answer here. I like to think of a duplicated draw as representing another member of the underlying population that has values close to the case that's being duplicated from your original sample. | |
| Sep 3, 2020 at 20:52 | answer | added | Tylerr | timeline score: 2 | |
| Sep 3, 2020 at 18:23 | comment | added | Dave | The gist of bootstrap is that you're treating the discrete, empirical distribution as a population. When you sample from a discrete distribution, you might get duplicated values. | |
| Sep 3, 2020 at 18:09 | history | asked | 24n8 | CC BY-SA 4.0 |