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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 { X i } be a set of random variables with a Rademacher distribution. Let { a i } 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 || a i ||2 is the Euclidean norm of the sequence { a i }, t is a real number > 0 and Pr (Z) is the probability of event Z .
Also if ||a i ||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 || a i ||1 is the 1-norm of the sequence { a i }.
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
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
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