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The generalized central limit theorem of Gnedenko-Levy describes the asymptotic behavior of a sum of IIDRVs which may not have finite mean or variance. Only a small class of limit laws can be realized, and Cauchy distributions are a notable example.

Steutel and van Harn introduced a notion of stability for RVs defined on $\mathbb{N}$: similarly to the "traditional" case (where characteristic functions are extensively utilized), this is done in terms of generating functions.

In my cursory survey, I have found oblique remarks to the effect that discrete stable distributions are very similar to their continuous analogues. I have, however, been unable to find a usable reference.

In my work I have recently come across a rather surprising bit of behavior. I have some RVs on $\mathbb{N}$ that (after a bit of reasonable kernel density estimation) are profoundly good fits to (discrete analogues of) Cauchy distributions (in fact, up to single-parameter scaling, the same distribution--which is remarkable because their peaks are not at the origin and rescale to each other). This is also pretty weird because--again--these RVs are defined on $\mathbb{N}$, not $\mathbb{Z}$. If the fits weren't so good, I'd toss it off as a coincidence, but I am (I think naturally) curious about the possibility that this is no accident. If the discrete stable distributions are close in a suitable sense to continuous analogues, I would have the seed of an explanation. So:

How similar are discrete and continuous stable distributions, and in particular is there something like a Cauchy distribution centered away from 0 that can occur as a discrete stable distribution supported on $\mathbb{N}$?

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    $\begingroup$ I don't think this answers your question but it might be helpful to point out that Nolan has the first chapter of his book on Levy-alpha stable distributions online here: academic2.american.edu/~jpnolan/stable/chap1.pdf . Perhaps you know this already but the Cauchy distribution is a Levy-stable distribution with $\alpha=1$ and (in Nolan's notation) skew parameter $\beta=0$. Setting the skew parameter $\beta=1$ makes it essentially zero to left of a certain point and one can adjust the shift, $\delta$ (again, in Nolan's notation), to taste. $\endgroup$ – dorkusmonkey Nov 4 '11 at 21:14
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I realise you asked this question over two years ago, but: The one-sided continuous-stable distributions can be directly linked to the discrete-stable distributions through a formulation called the 'Poisson transform', where the mean of a Poisson distribution is modulated by another distribution (one example of this is where the other distribution is a Gamma distribution - the resultant distribution is negative binomial).

In the case when the modulating distribution is one-sided continuous-stable of power-law index nu, the resultant distribution is discrete-stable with index nu.

I talk about this in depth in my thesis ("Continuous and discrete properties of stochastic processes") which also has an in-depth literature review you might find useful and, for a tl;dr version, in the paper "Continuous and discrete stable processes" (on Academia.edu or DOI link to APS.org).

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