Timeline for Finding the correct probability distribution function
Current License: CC BY-SA 3.0
16 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 8, 2018 at 17:50 | comment | added | JimB | I don't know why but I felt if I repeated myself from 2 years ago, it might have an effect. And note that whatever you end up with, it won't be "correct" (as requested in the title). It will be (hopefully) a reasonably parsimonious description of the data (no more, no less). | |
| Dec 8, 2018 at 17:46 | comment | added | JimB | Your data is clearly discrete (iintegers going from 4 to 29) and so fitting a continuous distribution is not likely a great approach. If you could mention something about the data generation process, that would help. For instance, are these numbers counts? Is there just a lot of round-off that results in integer values? And why you think you need to estimate a probability density or probability mass function, would also be helpful. In other words, what would having a function with a few parameters benefit you over just showing the histogram/frequency table? | |
| Dec 8, 2018 at 9:09 | comment | added | Alvaro Perea | It's a Weibull distribution. Note the aging effect at high values (curve asymmetry). | |
| Oct 28, 2016 at 15:19 | comment | added | JimB | If your integer data consists of counts, then attempting to fit continuous distributions will never get you the "correct" distribution (which is taken directly from your question). If your integer data is simply rounded, then treating your data as if it were continuous is not the best way to estimate the parameters in an underlying continuous distribution. So...what is the generating process of your data? As has been suggested by others in your previous question, are there not candidate distributions suggested or implied by your experiment? | |
| Oct 28, 2016 at 12:56 | vote | accept | Vaggelis_Z | ||
| Oct 28, 2016 at 12:47 | answer | added | corey979 | timeline score: 3 | |
| Oct 28, 2016 at 11:23 | history | edited | Vaggelis_Z | CC BY-SA 3.0 | added 13 characters in body |
| Oct 28, 2016 at 10:22 | history | edited | Vaggelis_Z | CC BY-SA 3.0 | added 9 characters in body |
| Oct 28, 2016 at 9:52 | review | Close votes | |||
| Oct 29, 2016 at 13:43 | |||||
| Oct 28, 2016 at 9:45 | history | edited | Vaggelis_Z | CC BY-SA 3.0 | edited body |
| Oct 28, 2016 at 9:39 | comment | added | corey979 | Have you tried FindDistribution? | |
| Oct 28, 2016 at 9:12 | history | edited | Vaggelis_Z | CC BY-SA 3.0 | deleted 5 characters in body |
| Oct 28, 2016 at 8:56 | history | edited | Vaggelis_Z | CC BY-SA 3.0 | added 3 characters in body |
| Oct 28, 2016 at 8:39 | comment | added | Feyre | "Why?" Because that would be an even worse fit, the distributions tie height with width. You can't just guess at the distributions, surely you have an idea which one would fit before you run the experiment? | |
| Oct 28, 2016 at 8:38 | history | edited | Vaggelis_Z | CC BY-SA 3.0 | added 1 character in body |
| Oct 28, 2016 at 8:29 | history | asked | Vaggelis_Z | CC BY-SA 3.0 |