All Questions
7
questions
1
vote
0
answers
139
views
Bootstrap estimation of variance and C.I. in cases with small group of outliers
I have a given quantity, say $y_a$ which parametrically depends on $a$ value. I consider $N$ values for the $a$ parameter and, for each one take multiple measures of the corresponding $y_a$ (say $N_a$ ...
- 21
0
votes
0
answers
133
views
Bootstrapping variance in R gives weird shaped distribution- how to obtain confidence intervals?
this is the first time I've used bootstrapping so it's quite basic!
I'm trying to obtain confidence intervals for the standardised variance- defined as the variance over the square of the mean- across ...
- 41
3
votes
1
answer
4k
views
Compute confidence interval for univariate Kernel Density Estimator
I've got a univariate dataset (timeseries) for two kind of simulated systems, and I want to explore the differences between the two.
To do that, I can build a univariate gaussian KDE for each dataset ...
- 31
3
votes
2
answers
8k
views
Computing confidence intervals for population variance from a sample in R
Is there a package available for R (on CRAN, github, r-forge, etc.) that computes CIs for the population variance, given a sample of data, 95% CI parameter, etc.?
The ...
- 143
12
votes
2
answers
3k
views
How can I pool bootstrapped p-values across multiply imputed data sets?
I am concerned with the problem that I would like to bootstrap the p-value for an estimate of $\theta$ from multiply imputed (MI) data, but that it is unclear to me how to combine the p-values across ...
- 6,624
2
votes
1
answer
471
views
Bootstrap confidence intervals for partitioned variances in R
I'm trying to determine the variance partitioning within plant drought tolerance data among hierarchical ecological levels, from species to forest sites to biome levels. I did that with the varcomp ...
- 43
7
votes
2
answers
2k
views
Using bootstrap for glm coefficients variance estimation (in R)
I am fitting a GLM model (in R), and would like to get an estimation of the variability of the coefficients estimated by the model.
If I understand it correctly the method to use in such a case is ...
- 21.8k