Rademacher distribution

The probability mass function of this distribution is

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

In terms of the Dirac delta function, as

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

Van Zuijlen has proved the following result.[2]

Let X_i be a set of independent Rademacher distributed random variables. Then

${\displaystyle \Pr \left(\left|{\frac {\sum _{i=1}^{n}X_{i}}{\sqrt {n}}}\right|\leq 1\right)\geq 0.5.}$

The bound is sharp and better than that which can be derived from the normal distribution (approximately Pr > 0.31).

Let {x_i} be a set of random variables with a Rademacher distribution. Let {a_i} be a sequence of real numbers. Then

${\displaystyle \Pr \left(\sum _{i}x_{i}a_{i}>t||a||_{2}\right)\leq e^{-{\frac {t^{2}}{2}}}}$

where ||a||₂ is the Euclidean norm of the sequence {a_i}, t > 0 is a real number and Pr(Z) is the probability of event Z.[3]

Let Y = Σ x_ia_i and let Y be an almost surely convergent series in a Banach space. The for t > 0 and s ≥ 1 we have[4]

${\displaystyle \Pr \left(||Y||>st\right)\leq \left[{\frac {1}{c}}\Pr(||Y||>t)\right]^{cs^{2}}}$

for some constant c.

Let p be a positive real number. Then the Khintchine inequality says that[5]

${\displaystyle c_{1}\left[\sum {\left|a_{i}\right|^{2}}\right]^{\frac {1}{2}}\leq \left(E\left[\left|\sum {a_{i}x_{i}}\right|^{p}\right]\right)^{\frac {1}{p}}\leq c_{2}\left[\sum {\left|a_{i}\right|^{2}}\right]^{\frac {1}{2}}}$

where c₁ and c₂ are constants dependent only on p.

For p ≥ 1,

${\displaystyle c_{2}\leq c_{1}{\sqrt {p}}.}$

See also: Concentration inequality - a summary of tail-bounds on random variables.

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.

Random vectors with components sampled independently from the Rademacher distribution are useful for various stochastic approximations, for example:

• The Hutchinson trace estimator,[6] which can be used to efficiently approximate the trace of a matrix of which the elements are not directly accessible, but rather implicitly defined via matrix-vector products.
• SPSA, a computationally cheap, derivative-free, stochastic gradient approximation, useful for numerical optimization.

Rademacher random variables are used in the Symmetrization Inequality.

• Bernoulli distribution: If X has a Rademacher distribution, then ${\displaystyle {\frac {X+1}{2}}}$ has a Bernoulli(1/2) distribution.
• Laplace distribution: If X has a Rademacher distribution and Y ~ Exp(λ), then XY ~ Laplace(0, 1/λ).