Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with bootstrap
Search options not deleted
user 6082
The bootstrap is a resampling method to estimate the sampling distribution of a statistic.
11
votes
Accepted
Estimate confidence interval of mean by bootstrap t method or simply by bootstrap?
trimming, percentile bootstrap outperforms the bootstrap-$t$ (the situation is unclear for 10% trimming). … Because we will soon meet much more accurate bootstrap intervals, our recommendation is that when bootstrap t and bootstrap percentile intervals do not agree closely, neither type of interval should be …
- 4,770
24
votes
Accepted
How can I calculate the confidence interval of a mean in a non-normally distributed sample?
The R package simpleboot is a good start:
library(simpleboot)
# 20% trimmed mean bootstrap
b1 <- one.boot(x, mean, R=2000, tr=.2)
boot.ci(b1, type=c("perc", "bca"))
... gives you following result: … # The bootstrap trimmed mean:
> b1$t0
[1] 1.144648
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 2000 bootstrap replicates
Intervals :
Level Percentile BCa
95% ( 1.062 …
- 4,770