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Jan 20, 2017 at 22:58 comment added Paul B. Slater Thanks, Glen-b. Well, I was speculating that the residuals might have some underlying structure--that since they are all nonnegative on [0,1]--might, at least, be proportional to one of the known probability distributions on that interval. In any case, this is all quite exploratory/tentative in nature.
Jan 20, 2017 at 3:37 comment added Glen_b Aren't you actually trying to fit your data rather than approximate the distribution that approximates the data (these residuals you mention)? (And if so, why are you trying to do that? What is the use to which you'll put the fitted distribution?)
Jan 19, 2017 at 19:55 history edited Paul B. Slater CC BY-SA 3.0
a third paragraph now added
Jan 19, 2017 at 19:47 review Close votes
Jan 21, 2017 at 13:33
Jan 19, 2017 at 19:43 history edited Paul B. Slater CC BY-SA 3.0
added paragraph--in response to the comment of whuber
Jan 19, 2017 at 19:18 comment added whuber I cannot match that distribution to your function unless I (a) change $\alpha$ to approximately $2.3$ and (b) divide it by approximately $47$. You seem only to be attempting to approximate a function of $\varepsilon$ by a formula of the form $C\varepsilon^{\alpha-1}(1-\varepsilon)^{\beta-1}$--but what does that have to do with "distributions," "mechanisms," or anything else statistical? It appears that for us to be able to address your question you would (at a minimum) have to explain what your point collection means.
Jan 19, 2017 at 19:18 comment added Tim Check en.wikipedia.org/wiki/Kumaraswamy_distribution
Jan 19, 2017 at 19:13 history edited Paul B. Slater CC BY-SA 3.0
added 45 characters in body
Jan 19, 2017 at 19:07 review First posts
Jan 19, 2017 at 19:19
Jan 19, 2017 at 19:03 history asked Paul B. Slater CC BY-SA 3.0