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Mar 30, 2015 at 13:11 comment added Scortchi (1) I didn't mean take logs of zero - work with the log-gamma function. (2) A high variance to mean ratio is a good reason to prefer the negative binomial to the Poisson; low probabilities of some individual observations isn't. Not sure why/how you're identifying individual observations as "significant".
Mar 30, 2015 at 13:01 comment added elsherbini In python numbers 0.0 < x < 1e323 are interpreted as 0.0, so taking the log yields -infinity. I rejected the poisson values because the mean and variance were three orders of magnitude apart, and knowing the underlying process it is likely inappropriate to identify the counts of so many observations as significant.
Mar 30, 2015 at 12:53 comment added Scortchi Not a p-value then, just a probability under an assumed distribution. And note that small probabilities of individual observations aren't in themselves evidence that the assumed distribution's not a good fit (think of the distribution of winning combinations in a lottery). Using logarithms would let you deal with them more easily.
Mar 30, 2015 at 12:26 comment added elsherbini Sorry, my data is a list of observations with counts asociated with each one. Something like [100, 42000, 1300, ...]. Once I have a ML estimate of the parameters for the distribution, I want to ask what the chances are of seeing 42000, for instance, by random chance.
Mar 30, 2015 at 11:07 comment added Scortchi Not quite sure what you mean by the p-value of a single observation.
Mar 30, 2015 at 10:39 answer added Scortchi timeline score: 2
Mar 29, 2015 at 21:38 comment added elsherbini After reading this blog post it appears that r=beta and p = alpha/(1-alpha) where alpha and beta are the parameters from the gamma distribution. So I guess I need to estimate alpha and beta somehow.
Mar 29, 2015 at 21:36 history edited elsherbini CC BY-SA 3.0
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Mar 29, 2015 at 21:20 history edited Andy
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Mar 29, 2015 at 21:09 review First posts
Mar 29, 2015 at 21:20
Mar 29, 2015 at 21:04 history asked elsherbini CC BY-SA 3.0