Timeline for Unbiased estimate of the variance of an *unnormalised* weighted mean
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Jun 14, 2023 at 16:30 | review | Suggested edits | |||
| Jun 15, 2023 at 1:11 | |||||
| Sep 29, 2015 at 19:18 | review | Late answers | |||
| Sep 30, 2015 at 13:07 | |||||
| S May 21, 2014 at 18:50 | history | suggested | mle | CC BY-SA 3.0 |
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| May 21, 2014 at 18:45 | review | Suggested edits | |||
| S May 21, 2014 at 18:50 | |||||
| Jun 2, 2011 at 7:20 | vote | accept | andybuckley | ||
| Jun 2, 2011 at 7:19 | comment | added | andybuckley | I concur that there seem to be quite differing opinions on what weights are for. In my case, they come from samplers used in physics code: to generate adequate statistical coverage for regions of the sampling phase space which are physically suppressed, the sampled function is multiplied by an enhancement function. The raw distributions are then unphysical, so sampled points need to be down-weighted by the relevant enhancement factor when computing observables: this weight needs to be propagated into the calculation of uncertainties. Hope that clarifies a bit. Thanks again :) | |
| Jun 2, 2011 at 7:14 | comment | added | andybuckley | Hi Kathy... thanks for the response, and sorry that mine has also taken a long time to get around to. For the particular problem that we had, I believe that we found a suitable solution some time ago by use of an "effective N", computed as $(\sum W_i)^2 / \sum W_i^2$. I'd have to have a bit of a think to see if that is equivalent to the substitutions that you propose. Some histogramming code that implements this scheme (and which produces reasonable-looking results) is here: projects.hepforge.org/rivet/trac/browser/trunk/include/LWH/… | |
| Mar 24, 2011 at 8:28 | history | answered | Kathy | CC BY-SA 2.5 |