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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>





Revision as of 08:28, 17 May 2013


Rademacher
Support k { 1 , 1 } {\displaystyle k\in \{-1,1\}\,}
PMF f ( k ) = { 1 / 2 , k = 1 1 / 2 , k = 1 {\displaystyle f(k)={\begin{cases}1/2,&k=-1\\1/2,&k=1\end{cases}}}
CDF F ( k ) = { 0 , k < 1 1 / 2 , 1 k < 1 1 , k 1 {\displaystyle F(k)={\begin{cases}0,&k<-1\\1/2,&-1\leq k<1\\1,&k\geq 1\end{cases}}}
Mean 0 {\displaystyle 0\,}
Median 0 {\displaystyle 0\,}
Mode N/A
Variance 1 {\displaystyle 1\,}
Skewness 0 {\displaystyle 0\,}
Excess kurtosis 2 {\displaystyle -2\,}
Entropy ln ( 2 ) {\displaystyle \ln(2)\,}
MGF cosh ( t ) {\displaystyle \cosh(t)\,}
CF cos ( t ) {\displaystyle \cos(t)\,}

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

f ( k ) = { 1 / 2 if  k = 1 , 1 / 2 if  k = + 1 , 0 otherwise. {\displaystyle f(k)=\left\{{\begin{matrix}1/2&{\mbox{if }}k=-1,\\1/2&{\mbox{if }}k=+1,\\0&{\mbox{otherwise.}}\end{matrix}}\right.}

It can be also written as a probability density function, in terms of the Dirac delta function, as

f ( k ) = 1 2 ( δ ( k 1 ) + δ ( k + 1 ) ) . {\displaystyle f(k)={\frac {1}{2}}\left(\delta \left(k-1\right)+\delta \left(k+1\right)\right).}

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

P r ( i X i a i > t | | a i | | 2 ) e t 2 2 {\displaystyle Pr(\sum _{i}X_{i}a_{i}>t||a_{i}||_{2})\leq e^{-{\frac {t^{2}}{2}}}}

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

P r ( i X i a i > t | | a i | | 1 ) = 0 {\displaystyle Pr(\sum _{i}X_{i}a_{i}>t||a_{i}||_{1})=0}

where || ai ||1 is the 1-norm of the sequence { ai }.

Related distributions

  • Bernoulli distribution: If X has a Rademacher distribution then X + 1 2 {\displaystyle {\frac {X+1}{2}}} has a Bernoulli(1/2) distribution.

References

  1. Hitczenko P, Kwapień S (1994) On the Rademacher series. Progress in probability 35: 31-36
  2. MontgomerySmith SJ (1990) The distribution of Rademacher sums. Proc Amer Math Soc 109: 517522
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