Timeline for Analyse data for Poisson
Current License: CC BY-SA 4.0
11 events
| when toggle format | what | by | license | comment | |
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| Jul 11, 2018 at 15:33 | answer | added | kjetil b halvorsen♦ | timeline score: 1 | |
| Jul 11, 2018 at 15:28 | history | edited | kjetil b halvorsen♦ | CC BY-SA 4.0 |
edited tags; edited tags
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| Aug 4, 2017 at 13:01 | history | edited | kjetil b halvorsen♦ | CC BY-SA 3.0 |
deleted 11 characters in body; edited tags
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| Jan 26, 2017 at 14:06 | review | Close votes | |||
| Jan 26, 2017 at 17:14 | |||||
| S Jan 26, 2017 at 10:08 | history | suggested | Tavrock | CC BY-SA 3.0 |
Added relevant tag; corrected word useage and syntax.
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| Jan 26, 2017 at 9:44 | comment | added | explorer | Do I need to include days without any emails (zero days) in data set? In simulation software I am using you can make custom behavior of user, you need to choose distribution (poisson or negative binomial) and inter_arrival time (exponentials in most cases). | |
| Jan 26, 2017 at 9:28 | comment | added | Roland | "What I plan to do is to simulate user email generation process in order to predict future behavior." This is not sufficient information. How do you plan to predict? Surely a distribution (there are several that model under/overdispersion and/or zero-inflation) alone is not a sufficient model. | |
| Jan 26, 2017 at 9:23 | comment | added | mdewey | You might want to explore the negative binomial instead of the Poisson which deals with over-dispersion. If you have an excess of zeroes there are methods for that too so perhaps expand your post if you need more help. | |
| Jan 26, 2017 at 9:18 | review | Suggested edits | |||
| S Jan 26, 2017 at 10:08 | |||||
| Jan 26, 2017 at 9:00 | review | First posts | |||
| Jan 26, 2017 at 9:23 | |||||
| Jan 26, 2017 at 9:00 | history | asked | explorer | CC BY-SA 3.0 |