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Any bivariate distribution function with marginal distribution functions F and G satisfies max{F(x)+G(y)-1,0}<=H(x,y)<=min{F(x),G(y)}.

gamma_r=(kappa_r)/(sigma^(r+2)), where kappa_r are cumulants and sigma is the standard deviation.

If a distribution has a single mode at mu_0, then P(|x-mu_0|>=lambdatau)<=4/(9lambda^2), where tau^2=sigma^2+(mu-mu_0)^2.

A set of statistical distributions having different variances.

A set of statistical distributions having the same variance.

A sampling phenomenon produced when a waveform is not sampled uniformly at an interval t each time, but rather at a series of slightly shifted intervals t+Deltat_i such that ...

A distribution with a high peak so that the kurtosis excess satisfies gamma_2>0.

Given a point set P={x_n}_(n=0)^(N-1) in the s-dimensional unit cube [0,1)^s, the local discrepancy is defined as D(J,P)=|(number of x_n in J)/N-Vol(J)|, Vol(J) is the ...

A minimization of the maximum error for a fixed number of terms.

A moment sequence is a sequence {mu_n}_(n=0)^infty defined for n=0, 1, ... by mu_n=int_0^1t^ndalpha(t), where alpha(t) is a function of bounded variation in the interval ...