Sub-Gaussian distribution
In probability theory, a sub-Gaussian distribution is a probability distribution with strong tail decay. Informally, the tails of a sub-Gaussian distribution are dominated by (i.e. decay at least as fast as) the tails of a Gaussian.
Formally, the probability distribution of a random variable X is called sub-Gaussian if there are positive constants C, v such that for every t > 0,
The sub-Gaussian random variables with the following norm form a Birnbaum–Orlicz space:
Equivalent properties
The following properties are equivalent:
- The distribution of X is sub-Gaussian
- Laplace transform condition:
- Moment condition:
- Union bound condition: where are i.i.d copies of X.
See also
References
- Kahane, J.P. (1960). "Propriétés locales des fonctions à séries de Fourier aléatoires". Stud. Math. 19. pp. 1–25. .
- Buldygin, V.V.; Kozachenko, Yu.V. (1980). "Sub-Gaussian random variables". Ukrainian Math. J. 32. pp. 483–489. .
- Ledoux, Michel; Talagrand, Michel (1991). Probability in Banach Spaces. Springer-Verlag.
- Stromberg, K.R. (1994). Probability for Analysts. Chapman & Hall/CRC.
- Litvak, A.E.; Pajor, A.; Rudelson, M.; Tomczak-Jaegermann, N. (2005). "Smallest singular value of random matrices and geometry of random polytopes" (PDF). Adv. Math. 195. pp. 491–523.
- Rudelson, Mark; Vershynin, Roman (2010). "Non-asymptotic theory of random matrices: extreme singular values". arXiv:1003.2990.
- Rivasplata, O. (2012). "Subgaussian random variables: An expository note" (PDF). Unpublished.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.