Timeline for Which binomial prediction interval works well for tail probabilities, i.e. $\hat{p}=1/n$ for large $n$
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| Mar 7, 2017 at 22:55 | history | edited | Sycorax♦ |
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| Mar 7, 2017 at 20:49 | vote | accept | Sycorax♦ | ||
| Mar 7, 2017 at 3:32 | history | tweeted | twitter.com/StackStats/status/838955517624090624 | ||
| Mar 6, 2017 at 23:28 | answer | added | whuber♦ | timeline score: 8 | |
| Mar 6, 2017 at 23:07 | history | edited | Sycorax♦ | CC BY-SA 3.0 |
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| Mar 6, 2017 at 22:54 | comment | added | Tim | @Sycorax so it seems I'm tired and have to go to sleep. It also makes a statistical joke: that for statisticians successes are basically the same as failures but seen from different perspective ;) | |
| Mar 6, 2017 at 22:51 | comment | added | Sycorax♦ | @Tim Yes, that was the very oblique joke I was making. :-) | |
| Mar 6, 2017 at 22:51 | comment | added | Tim | @Sycorax but it's basically the same, isn't it? | |
| Mar 6, 2017 at 22:49 | comment | added | Sycorax♦ | @tim it would be hilarious if there's also a large, entirely disjoint body of literature on "all success" data. | |
| Mar 6, 2017 at 22:47 | comment | added | Tim | You can also recall that if n is large, then 1/n is very close to observing no successes and there is pretty large literature on no-successes data, see stats.stackexchange.com/questions/134380/… | |
| Mar 6, 2017 at 22:44 | history | edited | Sycorax♦ | CC BY-SA 3.0 |
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| Mar 6, 2017 at 22:43 | comment | added | Tim | @Sycorax your reading is not flawed, I'm providing this one for reference since it is related but you are right that is is only about CI's. | |
| Mar 6, 2017 at 22:41 | history | edited | Sycorax♦ | CC BY-SA 3.0 |
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| Mar 6, 2017 at 22:41 | comment | added | Sycorax♦ | @Tim Yes, thank you. I had actually edited out that link in one of my edits. The A-C interval recommendation would appear to (1) only address the large $n$ condition but not the small $p$ condition and (2) refer to confidence intervals vice prediction intervals. My reading my be flawed. | |
| Mar 6, 2017 at 22:38 | comment | added | Tim | I guess you are familiar with this thread: stats.stackexchange.com/questions/82720/… , but posting it for reference (see also the quoted paper). | |
| Mar 6, 2017 at 22:34 | history | edited | Sycorax♦ | CC BY-SA 3.0 |
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| Mar 6, 2017 at 22:30 | comment | added | Sycorax♦ | @whuber Yes, that is correct: we will have some future data coming in and I'd like to estimate the probability that one of those new values falls below the sample minimum that I have today. | |
| Mar 6, 2017 at 22:09 | comment | added | whuber♦ | Your fourth (last) bullet suggests you aren't computing confidence intervals: you seem to be asking for the coverage of a prediction limit. Is that a correct interpretation? | |
| Mar 6, 2017 at 22:04 | history | asked | Sycorax♦ | CC BY-SA 3.0 |