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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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12 citations
TL;DR: In this article, the symmetrical Tukey lambda distributions are obtained by simple transformations of a uniformly distributed variable, and properties of these distributions are studied and a numerical illustration is presented.
Abstract: SUMMARY The symmetrical Tukey lambda distributions are obtained by simple transformations of a uniformly distributed variable. Systems of multivariate distributions can be formed by applying these transformations to sets of variables having a joint Dirichlet distribution. Since no more than one of such a set of variables can have a uniform distribution, though all have beta distributions, we are led to study distributions of transforms of variables having standard beta distributions. These distributions are termed extended Tukey lambda distributions. Properties of these distributions are studied. Properties of the multivariate distributions are also described and a numerical illustration is presented.
12 citations
TL;DR: In this paper, a new class of probability distributions is introduced, which accentuates the periodic behaviour of environmental conditions in time and the random occurrence of some events on each period, following the logic of the appearance of random events in the evolution of real-world systems.
Abstract: A new class of probability distributions is introduced. This class accentuates the periodic behaviour of environmental conditions in time and the random occurrence of some events on each period. A constructive approach is used following the logic of the appearance of random events in the evolution of real-world systems. Some physical and probabilistic properties of the new distributions are discussed. Elementary statistical data models are considered and two types of estimations of the attributes of these distributions are offered. Numerical and graphical illustrations with simulated and observed data are used to motivate our suggestions.
12 citations
01 Jan 2001
TL;DR: In this paper, it was shown that in general, we need all closed convex classes of probability distributions, i.e., classes of all distributions which are located on a given interval.
Abstract: Traditionally, in science and engineering, measurement uncertainty is characterized by a probability distribution; however, often, we don’t know this probability distribution exactly, so we must consider classes of possible probability distributions. Interval computations deal with a very specific type of such classes: classes of all distributions which are located on a given interval. We show that in general, we need all closed convex classes of probability distributions.
11 citations
01 Jan 2000
11 citations