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<math> Pr( \sum_i X_i a_i > t || a_i ||_2 ) \le e^{ - \frac{ t^2 }{ 2 } } </math> | <math> Pr( \sum_i X_i a_i > t || a_i ||_2 ) \le e^{ - \frac{ t^2 }{ 2 } } </math> | ||
where || ''a''<sub>i</sub>||<sub>2</sub> is the ] of the sequence { ''a''<sub>i</sub> }, ''t'' is a real number and ''Pr''(Z) is the probability of event ''Z''.<ref name=MontgomerySmith1990>MontgomerySmith SJ (1990) The distribution of Rademacher sums. Proc Amer Math Soc 109: 517522</ref> | where || ''a''<sub>i</sub>||<sub>2</sub> is the ] of the sequence { ''a''<sub>i</sub> }, ''t'' is a real number > 0 and ''Pr''(Z) is the probability of event ''Z''.<ref name=MontgomerySmith1990>MontgomerySmith SJ (1990) The distribution of Rademacher sums. Proc Amer Math Soc 109: 517522</ref> | ||
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In probability theory and statistics, the Rademacher distribution (named after Hans Rademacher) is a discrete probability distribution which has a 50% chance for either 1 or -1.
Mathematical formulation
The probability mass function of this distribution is
It can be also written as a probability density function, in terms of the Dirac delta function, as
Applications
The Rademacher distribution has been used in bootstrapping.
The Rademacher distribution can be used to show that normally distributed and uncorrelated does not imply independent.
Bounds on sums
Let { Xi } be a set of random variables with a Rademacher distribution. Let { ai } be a sequence of real numbers. Then
where || ai||2 is the Euclidean norm of the sequence { ai }, t is a real number > 0 and Pr(Z) is the probability of event Z.
Also if ||ai||1 is finite then
where || ai ||1 is the 1-norm of the sequence { ai }.
Related distributions
- Bernoulli distribution: If X has a Rademacher distribution then has a Bernoulli(1/2) distribution.
References
- Hitczenko P, Kwapień S (1994) On the Rademacher series. Progress in probability 35: 31-36
- MontgomerySmith SJ (1990) The distribution of Rademacher sums. Proc Amer Math Soc 109: 517522