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Stable Distribution

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Encyclopedia of Database Systems
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Synonyms

Lévy skew α-stable distribution

Definition

A random variable Z is said to follow a symmetric α-stable distribution [13, 15], where 0 < α ≤ 2, if the Fourier transform of its probability density function fZ (z) satisfies

$$ {\int}_{-\infty}^{\infty }{e}^{\sqrt{-1} zt}{f}_Z(z) dt={e}^{-d\left|t\right|{}^{\alpha }},\,\, 0<\alpha \le 2 $$
(1)

where d > 0 is the scale parameter. This is denoted by ZS(α, d).

There is an equivalent definition. A random variable Z follows a symmetric α-stable distribution if, for any real numbers, C1 and C2,

$$ {C}_1{Z}_1+{C}_2{Z}_2\overset{d}{=}{\left(|{C}_1|{}^{\alpha }+|{C_2}^{\alpha}\right)}^{1/\alpha }Z, $$
(2)

where Z1 and Z2 are independent copies of Z, and the symbol “\( \overset{d}{=} \)” denotes equality in distribution.

The probability density function fZ (z) can be obtained by taking inverse Fourier transform of 1. In particular, fZ (z) can be expressed in closed-forms when α = 2 (i.e., the normal distribution) and α= 1 (i.e., the...

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  1. Achlioptas D. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J Comput Syst Sci. 2003;66(4):671–87.

    Article  MathSciNet  MATH  Google Scholar 

  2. Alon N, Matias Y, Szegedy M. The space complexity of approximating the frequency moments. In: Proceedings of the 28th Annual ACM Symposium on Theory of Computing; 1996. p. 20–9.

    Google Scholar 

  3. Cormode G, Datar M, Indyk P, Muthukrishnan S. Comparing data streams using hamming norms (how to zero in). In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 335–45.

    Chapter  Google Scholar 

  4. Datar M, Immorlica N, Indyk P, Mirrokn VS. Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the 20th Annual Symposium on Computational Geometry; 2004. p. 253–62.

    Google Scholar 

  5. Donoho DL. Compressed sensing. IEEE Trans Inform Theory. 2006;52(4):1289–306.

    Article  MathSciNet  MATH  Google Scholar 

  6. Fama EF, Roll R. Parameter estimates for symmetric stable distributions. J Am Stat Assoc. 1971;66(334):331–8.

    Article  MATH  Google Scholar 

  7. Indyk P. Stable distributions, pseudorandom generators, embeddings, and data stream computation. J ACM. 2006;53(3):307–23.

    Article  MathSciNet  MATH  Google Scholar 

  8. Indyk P, Motwani R. Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the 30th Annual ACM Symposium on Theory of Computing; 1998. p. 604–13.

    Google Scholar 

  9. Johnson WB, Lindenstrauss J. Extensions of Lipschitz mapping into Hilbert space. Contemp Math. 1984;26(189–206):1–1.1.

    Article  MathSciNet  MATH  Google Scholar 

  10. Li P. Very sparse stable random projections for dimension reduction in lα (0 < α ≤ 2) norm. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2007.

    Google Scholar 

  11. Li P. Estimators and tail bounds for dimension reduction in lα (0 < α ≤ 2) using stable random projections. In: Proceedings of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms; 2008.

    Google Scholar 

  12. Muthukrishnan S. Data streams: algorithms and applications. Found Trends Theor Comput Sci. 2005;1(2):117–236.

    Article  MathSciNet  MATH  Google Scholar 

  13. Samorodnitsky G, Taqqu MS. Stable Non-Gaussian random processes: Chapman & Hall; 1994.

    Google Scholar 

  14. Vempala S. The random projection method. Providence: American Mathematical Society; 2004.

    MATH  Google Scholar 

  15. Zolotarev VM. One-dimensional stable distributions. Providence: American Mathematical Society; 1986.

    Book  MATH  Google Scholar 

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Li, P. (2018). Stable Distribution. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_367

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