Discrete-stable distribution

Discrete-stable distributions ^[1] are a class of probability distributions with the property that the sum of several random variables from such a distribution under appropriate scaling is distributed according to the same family. They are the discrete analogue of continuous-stable distributions.

Discrete-stable distributions have been used in numerous fields, in particular in scale-free networks such as the internet and social networks ^[2] or even semantic networks. ^[3]

Both discrete and continuous classes of stable distribution have properties such as infinite divisibility, power law tails, and unimodality.

The most well-known discrete stable distribution is the special case of the Poisson distribution. ^[4] It is the only discrete-stable distribution for which the mean and all higher-order moments are finite.^{[ dubious – discuss ]}

Definition

The discrete-stable distributions are defined ^[5] through their probability-generating function

${\displaystyle G(s|\nu ,a)=\sum _{n=0}^{\infty }P(N|\nu ,a)(1-s)^{N}=\exp(-as^{\nu }).}$

In the above, ${\displaystyle a>0}$ is a scale parameter and ${\displaystyle 0<\nu \leq 1}$ describes the power-law behaviour such that when ${\displaystyle 0<\nu <1}$,

${\displaystyle \lim _{N\to \infty }P(N|\nu ,a)\sim {\frac {1}{N^{\nu +1}}}.}$

When ${\displaystyle \nu =1}$, the distribution becomes the familiar Poisson distribution with the mean ${\displaystyle a}$.

The characteristic function of a discrete-stable distribution has the form ^[6]

${\displaystyle \varphi (t;a,\nu )=\exp \left[a\left(e^{it}-1\right)^{\nu }\right]}$, with ${\displaystyle a>0}$ and ${\displaystyle 0<\nu \leq 1}$.

Again, when ${\displaystyle \nu =1}$, the distribution becomes the Poisson distribution with mean ${\displaystyle a}$.

The original distribution is recovered through repeated differentiation of the generating function:

${\displaystyle P(N|\nu ,a)=\left.{\frac {(-1)^{N}}{N!}}{\frac {d^{N}G(s|\nu ,a)}{ds^{N}}}\right|_{s=1}.}$

A closed-form expression using elementary functions for the probability distribution of the discrete-stable distributions is not known except for in the Poisson case in which

${\displaystyle \!P(N|\nu =1,a)={\frac {a^{N}e^{-a}}{N!}}.}$

Expressions exist, however, that use special functions for the case ${\displaystyle \nu =1/2}$ ^[7] (in terms of Bessel functions) and ${\displaystyle \nu =1/3}$ ^[8] (in terms of hypergeometric functions).

As compound probability distributions

The entire class of discrete-stable distributions can be formed as Poisson compound probability distribution where the mean, ${\displaystyle \lambda }$, of a Poisson distribution is defined as a random variable with a probability density function (PDF). When the PDF of the mean is a one-sided continuous-stable distribution with the stability parameter ${\displaystyle 0<\alpha <1}$ and scale parameter ${\displaystyle c}$, the resultant distribution is ^[9] discrete-stable with index ${\displaystyle \nu =\alpha }$ and scale parameter ${\displaystyle a=c\sec(\alpha \pi /2)}$.

Formally, this is written

${\displaystyle P(N|\alpha ,c\sec(\alpha \pi /2))=\int _{0}^{\infty }P(N|1,\lambda )p(\lambda$ ;\alpha ,1,c,0)\,d\lambda }

where ${\displaystyle p(x;\alpha ,1,c,0)}$ is the pdf of a one-sided continuous-stable distribution with symmetry parameter ${\displaystyle \beta =1}$ and location parameter ${\displaystyle \mu =0}$.

A more general result ^[8] states that forming a compound distribution from any discrete-stable distribution with index ${\displaystyle \nu }$ with a one-sided continuous-stable distribution with index ${\displaystyle \alpha }$ results in a discrete-stable distribution with index ${\displaystyle \nu \cdot \alpha }$ and reduces the power-law index of the original distribution by a factor of ${\displaystyle \alpha }$.

In other words,

${\displaystyle P(N|\nu \cdot \alpha ,c\sec(\pi \alpha /2))=\int _{0}^{\infty }P(N|\alpha ,\lambda )p(\lambda$ ;\nu ,1,c,0)\,d\lambda .}

Poisson limit

In the limit ${\displaystyle \nu \rightarrow 1}$, the discrete-stable distributions behave ^[9] like a Poisson distribution with mean ${\displaystyle a\sec(\nu \pi /2)}$ for small ${\displaystyle N}$, but for ${\displaystyle N\gg 1}$, the power-law tail dominates.

The convergence of i.i.d. random variates with power-law tails ${\displaystyle P(N)\sim 1/N^{1+\nu }}$ to a discrete-stable distribution is extraordinarily slow ^[10] when ${\displaystyle \nu \approx 1}$, the limit being the Poisson distribution when ${\displaystyle \nu >1}$ and ${\displaystyle P(N|\nu ,a)}$ when ${\displaystyle \nu \leq 1}$.

See also

• Stable distribution
• Poisson distribution

References

1. Steutel, F. W.; van Harn, K. (1979). "Discrete Analogues of Self-Decomposability and Stability" (PDF). Annals of Probability. 7 (5): 893–899. doi: 10.1214/aop/1176994950 .
2. Barabási, Albert-László (2003). Linked: how everything is connected to everything else and what it means for business, science, and everyday life. New York, NY: Plum.
3. Steyvers, M.; Tenenbaum, J. B. (2005). "The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth". Cognitive Science. 29 (1): 41–78. arXiv: cond-mat/0110012 . doi:10.1207/s15516709cog2901_3. PMID   21702767. S2CID   6000627.
4. Renshaw, Eric (2015-03-19). Stochastic Population Processes: Analysis, Approximations, Simulations. OUP Oxford. ISBN   978-0-19-106039-7.
5. Hopcraft, K. I.; Jakeman, E.; Matthews, J. O. (2002). "Generation and monitoring of a discrete stable random process". Journal of Physics A. 35 (49): L745–752. Bibcode:2002JPhA...35L.745H. doi:10.1088/0305-4470/35/49/101.
6. Slamova, Lenka; Klebanov, Lev. "Modeling financial returns by discrete stable distributions" (PDF). International Conference Mathematical Methods in Economics. Retrieved 2023-07-07.
7. Matthews, J. O.; Hopcraft, K. I.; Jakeman, E. (2003). "Generation and monitoring of discrete stable random processes using multiple immigration population models". Journal of Physics A. 36 (46): 11585–11603. Bibcode:2003JPhA...3611585M. doi:10.1088/0305-4470/36/46/004.
8. Lee, W.H. (2010). Continuous and discrete properties of stochastic processes (PhD thesis). The University of Nottingham.
9. Lee, W. H.; Hopcraft, K. I.; Jakeman, E. (2008). "Continuous and discrete stable processes". Physical Review E. 77 (1): 011109–1 to 011109–04. Bibcode:2008PhRvE..77a1109L. doi:10.1103/PhysRevE.77.011109. PMID   18351820.
10. Hopcraft, K. I.; Jakeman, E.; Matthews, J. O. (2004). "Discrete scale-free distributions and associated limit theorems". Journal of Physics A. 37 (48): L635 –L642. Bibcode:2004JPhA...37L.635H. doi:10.1088/0305-4470/37/48/L01.

Further reading

• Feller, W. (1971) An Introduction to Probability Theory and Its Applications, Volume 2. Wiley. ISBN   0-471-25709-5
• Gnedenko, B. V.; Kolmogorov, A. N. (1954). Limit Distributions for Sums of Independent Random Variables . Addison-Wesley.
• Ibragimov, I.; Linnik, Yu (1971). Independent and Stationary Sequences of Random Variables. Wolters-Noordhoff Publishing Groningen, The Netherlands.