Topic
K-distribution
About: K-distribution is a research topic. Over the lifetime, 1281 publications have been published within this topic receiving 51774 citations.
Papers published on a yearly basis
Papers
More filters
30 Apr 1968
TL;DR: In this paper, the exact distributions for the three Renyi-type statistics are given for the limiting and exact distributions of these three statistics, and applications of the tables are discussed and examples are given.
Abstract: : Tables are given of exact distributions for the three Renyi-type statistics. Expressions are given for the limiting and for the exact distributions of these three statistics. Applications of the tables are discussed and examples are given.
1 citations
TL;DR: In this article, a family of compound GPSDs with bivariate geometric compounding distribution is introduced, and the probability mass function, recursion formulas, conditional distributions and some properties are given.
Abstract: The family of Inated-parameter Generalized Power Series distributions (IGPSD) was introduced by Minkova in 2002 as a compound Generalized Power Series distributions (GPSD) with geometric compounding distribution. In these notes we introduce a family of compound GPSDs with bivariate geometric compounding distribution. The probability mass function, recursion formulas, conditional distributions and some properties are given. A member of this family is a Type II bivariate Polya-Aeppli distribution, introduced by Minkova and Balakrishanan (2014). In this notes the particular cases of bivariate compound binomial, negative binomial and logarithmic series distributions are analyzed in detail.
1 citations
TL;DR: In this paper, the cumulative probability Pn of occurence of some event is defined as the probability that it occurs at least once in n trials, where p is the probability density function of the single trial probabilities, the p's.
Abstract: Define the cumulative probability Pn of occurence of some event as the probability that it occurs at least once in n trials. If the single trial probabilities are not fixed, but are drawn from a distribution of their own, or are dependent on some other nonfixed variable, then it would be convenient to have an ?effective single trial probability (esp)? for use in the simple Bernoulli model which would give the same cumulative probability of occurence as actually observed. We show here that the esp can be interpreted as a kind of average, and that its value is given by 1 minus the geometric mean of 1-d(p), where d(p) is the probability density function of the single trial probabilities, the p's. We further define this geometric mean for both discrete and continuous distributions and evaluate the esp for several cases. These results are compared with earlier ones which suggest that the esp is given by the (arithmetic) average or expectation of the single trial probabilities, and we determine under what conditions this simpler result can be used.
1 citations