Rademacher distribution

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Rademacher
Probability mass function
Cumulative distribution function
Parameters
Support k=\{-1,1\}\,
Probability mass function (pmf) \begin{matrix}     1/2 & \mbox{for }k=-1 \\1/2 & \mbox{for }k=1     \end{matrix}
Cumulative distribution function (cdf) \begin{matrix}     0 & \mbox{for }k<-1 \\1/2 & \mbox{for }-1<k<1\\1 & \mbox{for }k>1     \end{matrix}
Mean 0\,
Median 0\,
Mode N/A
Variance 1\,
Skewness 0\,
Excess kurtosis -2\,
Entropy \ln(2)\,
Moment-generating function (mgf) \cosh(t)\,
Characteristic function \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. The probability mass function of this distribution is

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

The Rademacher distribution has been used in bootstrapping.

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Univariate Multivariate
Discrete: BenfordBernoullibinomialBoltzmanncategoricalcompound PoissondegenerateGauss-Kuzmingeometrichypergeometriclogarithmicnegative binomialparabolic fractalPoissonRademacherSkellamuniformYule-SimonzetaZipfZipf-Mandelbrot Ewensmultinomialmultivariate Polya
Continuous: BetaBeta primeCauchychi-squareDirac delta functionErlangexponentialexponential powerFfadingFisher's zFisher-TippettGammageneralized extreme valuegeneralized hyperbolicgeneralized inverse GaussianHalf-LogisticHotelling's T-squarehyperbolic secanthyper-exponentialhypoexponentialinverse chi-squareinverse Gaussianinverse gammaKumaraswamyLandauLaplaceLévyLévy skew alpha-stablelogisticlog-normalMaxwell-BoltzmannMaxwell speednormal (Gaussian)normal inverse GaussianParetoPearsonpolarraised cosineRayleighrelativistic Breit-WignerRiceshifted GompertzStudent's ttriangulartype-1 Gumbeltype-2 GumbeluniformVariance-GammaVoigtvon MisesWeibullWigner semicircleWilks' lambda DirichletKentmatrix normalmultivariate normalmultivariate Studentvon Mises-FisherWigner quasiWishart
Miscellaneous: Cantorconditionalexponential familyinfinitely divisiblelocation-scale familymarginalmaximum entropyphase-typeposteriorpriorquasisamplingsingular
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