Sentence examples for distributions possess from inspiring English sources

Our tests work with a wide range of time series including those whose marginal distributions possess heavy tails.

Note also that the distributions possess Gaussian-like shapes.

It is realized that many multivariate distributions possess the NSD property exhibited in practical examples.

A number of well-known multivariate distributions possess the NA property, such as the multinomial distribution, multivariate hypergeometric distribution, negatively correlated normal distribution, and joint distribution of ranks.

Joag-Dev and Proschan [1] introduced the definition of negative association and showed that many of the well-known multivariate distributions possess the negatively associated property, for example, multivariate hypergeometric distribution, negatively correlated normal distribution, random sampling without replacement, etc.

As pointed out and proved by Joag-Dev and Proschan [2], a number of well-known multivariate distributions possess the negative association property, such as multinomial, convolution of unlike multinomial, multivariate hypergeometric, Dirichlet, permutation distribution, negatively correlated normal distribution, random sampling without replacement, and joint distribution of ranks.

Parallel distributions possessed low parameter values and variability, whereas nonparallel distributions displayed an increase in values and variability.

The next characterization theorem gives the joint survival function of bivariate continuous distributions possessing LS-BLMP.

Without some simplifying assumptions on B(x 1,x 2 t) the class of bivariate distributions possessing S-BLMP is too cumbersome to be of use.

Joag-Dev and Proschan [2] pointed out that a number of well-known multivariate distributions possesses the negative association property, such as multinomial, convolution of unlike multinomial, multivariate hypergeometric, Dirichlet, permutation distribution, negatively correlated normal distribution, random sampling without replacement, and joint distribution of ranks.

Thus, one can find many examples of bivariate continuous distributions possessing B L M P 1 and B L M P 2 represented by (1) and (4), respectively, as well as those exhibiting positive or negative quadrant dependence that belong to the class (mathcal {L}(mathbf {x};mathbf {a})) specified by (6).