Topic
K-distribution
About: K-distribution is a research topic. Over the lifetime, 1281 publications have been published within this topic receiving 51774 citations.
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TL;DR: In this article, the authors measure fidelity of a single-rou asymmetric communication protocol by the relative entropy, which is the probability that the client will send more bits than the server wants to send.
25 citations
25 citations
TL;DR: In this paper, two families of discrete distributions, one with the probability generating function (p.f) and the other with the p.f. have been examined and relations among different members of the latter class are considered graphically by means of a criterion based on successive probabilities.
Abstract: Two families of discrete distributions, one with the probability generating function (p.g.f) and the other with the p.g.f. have been examined. Under certain restrictions on the parameters, some members of the former class can be interpreted as compound distributions. These resulting distributions have the property of being over-, under-, or equi-dispersed. These properties are also examined for the latter class of distributions. Relationships among different members of the latter class are considered graphically by means of a criterion based on successive probabilities.
25 citations
TL;DR: In this paper, the extreme value distribution of the skew-symmetric family is derived, where the probability density function of the latter is defined as twice the product of a symmetric density and a skewing function.
Abstract: We derive the extreme value distribution of the skew-symmetric family, the probability density function of the latter being defined as twice the product of a symmetric density and a skewing function. We show that, under certain conditions on the skewing function, this extreme value distribution is the same as that for the symmetric density. We illustrate our results using various examples of skew-symmetric distributions as well as two data sets.
25 citations
TL;DR: The S-distribution as discussed by the authors is defined by the ordinary differential equation dF/dX = α(Fg − FhFo = F(Xo), where F is the cumulative distribution of the random variable X, and α, g, h, and Fo are parameters.
Abstract: The S-distribution is defined by the ordinary differential equation dF/dX = α(Fg — FhFo = F(Xo), where F is the cumulative distribution of the random variable X, and α, g, h, and Fo are parameters. The S-distribution was recently described in this journal as a tool for the approximation and classification of univariate, unimodal continuous probability distributions. This article shows that the S-distribution rather accurately models the commonly used univariate discrete distributions.
24 citations