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 paper, a family of distributions is presented, including those of the exponential, Bernoulli and uniform distributions, and properties of the distribution and methods of parameter determination are developed.
Abstract: Methods are well-known for generating random values from many common statistical distributions. These common distributions are sometimes used in simulation studies due to the lack of convenient methods of generating random values from distributions having more arbitrary shapes. A family of distributions is presented here which assumes many shapes, including those of the exponential, Bernoulli and uniform distributions. Any given first four moments may be obtained through manipulation of four parameters. The inverse cdf exists in closed form, allowing straightforward generation of random values given a source of U(0,1) values. Properties of the distribution and methods of parameter determination are developed.
93 citations
TL;DR: An improved non-parametric method to estimate wind speed probability distributions based on the diffusion partial differential equation in finite domain, which accounts for both bandwidth selection and boundary correction of kernel density estimation.
93 citations
01 Apr 1999-Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment
Abstract: A prescription is defined for the interpolation of probability distributions that are assumed to have a linear dependence on a parameter of the distributions. The distributions may be in the form of continuous functions or histograms. The prescription is based on the weighted mean of the inverses of the cumulative distributions between which the interpolation is made. The result is particularly elegant for a certain class of distributions, including the normal and exponential distributions, and is useful for the interpolation of Monte Carlo simulation results which are time-consuming to obtain.
93 citations
TL;DR: Two distinct versions of the generalized beta distribution of the second kind are considered in this article, and they compare favorably with the commonly used gamma and log normal distributions in their ability to fit selected sets of accumulated streamflow and precipitation amount data.
Abstract: Two distinct versions of the generalized beta distribution of the second kind are considered. These beta-type distributions compare favorably with the commonly used gamma and log normal distributions in their ability to fit selected sets of accumulated streamflow and precipitation amount data. The comparisons are based on empirical results associated with three different goodness of fit criteria. Since the cumulative distribution functions of these beta-type distributions are in closed form, they possess unique computational advantages over the gamma and log normal distributions.
92 citations
TL;DR: In this article, a new family of distributions for non-negative data, defined by means of a quantile function, is introduced, which is applied to an example from environmental engineering.
Abstract: We introduce a new, flexible family of distributions for non-negative data, defined by means of a quantile function. We describe some properties of this family, and discuss several methods for estimating the parameters. The distribution is applied to an example from environmental engineering.
91 citations