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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01 Jan 1988
TL;DR: In this paper, the authors derived the familiar limiting distributions of excess, age, and spread on sample paths, where these distributions are fractions of time, and obtained similar results for the distribution of remaining service at a queue.
Abstract: The familiar limiting distributions of excess, age, and spread are derived on sample paths, where these distributions are fractions of time. Similar results are obtained for the distribution of remaining service at a queue. For stochastic processes, where specified limits exist as finite constants with probability 1, the derived sample-path results are shown to imply that the same limiting distributions hold, where in a stochastic setting, these distributions may be defined in a more conventional way.
TL;DR: In this paper, the proximity between exponential distributions and life distributions in various W-type classes was studied, where W type classes indicate DFR, DFRA, NWU, NWUE, IMRL and HNWUE.
Abstract: This paper is a continuation of the foregoing one [1]. In this paper, we study the proximity between exponential distributions and life distributions in variousW-type classes, whereW-type classes indicate DFR, DFRA, NWU, NWUE, IMRL and HNWUE.
TL;DR: In this paper , a transformer function is applied to three well-known probability distributions (Normal, Kumaraswamy, Weibull) and new extensions of these distributions are obtained, the related functions of the new extensions are defined, random samples are generated from the new extension and the results are presented.
Abstract: In this paper, generating extension forms for continuous probability distribution functions is studied. The considered transformer function is applied to three well-known probability distributions- Normal, Kumaraswamy, Weibull- and new extensions of these distributions are obtained. The related functions of the new extensions are defined, random samples are generated from the new extensions and the results are presented. Parameter estimation procedures of the extensions are studied, and likelihood equations are obtained. To demonstrate the modeling performance of the extensions, three different data sets are considered, separately. Each data set is modeled by both the corresponding probability distribution and its extensions. The new extensions give the best fit to the corresponding data over the well-known probability distributions.
TL;DR: This problem of the recovery of probability distributions of random functions that describe images dynamically generated from signals received from synthetic aperture radars is examined and a method of recovery is developed.
Abstract: We examine the problem of the recovery of probability distributions of random functions that describe images dynamically generated from signals received from synthetic aperture radars. This problem is shown to have a unique solution within a specific class of random functions. A method of recovery is developed.
01 Jan 1991
TL;DR: In this article, the notion of probability distributions and associated characteristic functions over a set of noncommuting variables is studied and the applicability of the Marcinkiewicz theorem is explored.
Abstract: The notion of probability distributions and associated characteristic functions over a set of noncommuting variables are studied. With the provision that the marginal distributions restricted to a commuting set should give the nonnegative probability distributions over those variables we find that such generalized master distributions exist which, while incorporating implicit positivity requirements, are not themselves pointwise necessarily nonnegative. The cumulant generating functions are studied and the applicability of the Marcinkiewicz theorem explored.