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Results tagged with bootstrap
Search options questions only
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user 2798
The bootstrap is a resampling method to estimate the sampling distribution of a statistic.
8
votes
1
answer
778
views
Working with the bootstrap sample vs the original sample
Get a bootstrap sample of the input sample. That is, sample with replacement (e.g. get 100 resamples) and compute the mean for each resample. … Bootstrap vs original sample:
While I understand what approach #1 does. Is there an underlying estimator behind #2? …
- 19.7k
18
votes
3
answers
33k
views
Questions on parametric and non-parametric bootstrap
The section on bootstrap reads:
The bootstrap is a simple Monte Carlo technique to approximate the
sampling distribution. … Finally, I don't quite understand the difference between parametric and non-parametric bootstrap from this text. …
- 19.7k
31
votes
2
answers
21k
views
Mean of the bootstrap sample vs statistic of the sample
Say I have a sample and the bootstrap sample from this sample for a stastitic $\chi$ (e.g. the mean). … As we all know, this bootstrap sample estimates the sampling distribution of the estimator of the statistic. …
- 19.7k
19
votes
2
answers
21k
views
Understanding bootstrapping for validation and model selection
across its bootstrap samples?
Are there any texts that discuss how to use bootstrapping for model selection or validation? …
- 19.7k
7
votes
1
answer
1k
views
Bias and variance estimation with boostrap
Bias-correction of $\theta$:
$
{\bar {\theta }}_{{\mathrm {BiasCorrected}}}=N{\bar {\theta }}-(N-1){\bar {\theta }}_{{Jack}}
$
My question is: What are the corresponding formulas for bootstrap? …
- 19.7k
4
votes
1
answer
789
views
The Bayesian approach to computing estimator bias and variance
From what I understand, jackknife and bootstrapping are frequentist methods for computing statistics (bias, variance, etc.) of an estimator.
Given a sample of my data and an estimator, and assuming l …
- 19.7k