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Mar 13, 2018 at 20:59 vote accept McLawrence
Mar 13, 2018 at 20:59 vote accept McLawrence
Mar 13, 2018 at 20:59
Mar 13, 2018 at 20:59 vote accept McLawrence
Mar 13, 2018 at 20:59
Feb 26, 2018 at 19:30 answer added JimB timeline score: 3
Feb 26, 2018 at 13:59 answer added probabilityislogic timeline score: 4
Feb 26, 2018 at 8:02 comment added McLawrence @JimB I need some estimate for the error of my variance. By bootstrapping do you mean something like this: $Var[\hat{\theta}] = \frac{\sum_b^B \hat{\theta}_b - \hat{\bar{\theta}}}{B-1}$? How can I use my knowledge of $\sigma_\epsilon$ in this approach?
Feb 26, 2018 at 7:15 history tweeted twitter.com/StackStats/status/968021634824228865
Feb 26, 2018 at 4:12 comment added JimB As you've described it the variance of the samples is $\sigma^2+\sigma_\epsilon^2$. So if you know $\sigma_\epsilon^2$, you can subtract that from the variance of the samples. (Estimating the precision of that estimate of $\sigma^2$ is another matter.) If you don't know $\sigma_\epsilon^2$, then you can only estimate the sum of the two variances. One way that allows you to get separate estimates is to take 2 or more estimates/measurements for each sample. Then you can use a mixed model to get the separate estimates. If you know $\sigma_\epsilon^2$, why not consider a bootstrap approach?
Feb 25, 2018 at 21:12 comment added McLawrence @probabilityislogic Can you elaborate on how you would use that or refer me to some helpful material?
Feb 25, 2018 at 21:12 history edited McLawrence CC BY-SA 3.0
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Feb 25, 2018 at 21:05 comment added probabilityislogic One aspect that may help, is that you can create a pivotal quantity for this problem.
Feb 25, 2018 at 19:52 history edited McLawrence CC BY-SA 3.0
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S Feb 25, 2018 at 19:51 history suggested zimmerrol CC BY-SA 3.0
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Feb 25, 2018 at 19:50 review Suggested edits
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Feb 25, 2018 at 19:28 review First posts
Feb 25, 2018 at 19:36
Feb 25, 2018 at 19:27 history asked McLawrence CC BY-SA 3.0