K-distribution

About: K-distribution is a research topic. Over the lifetime, 1281 publications have been published within this topic receiving 51774 citations.

Estimators and distributions in single and multi-look polarimetric and interferometric data

Abstract: Provides a nearly complete analysis of the key distributions encountered in single and multi-look polarimetric and interferometric synthetic aperture radar (SAR) data, under a Gaussian or multi-variate K distribution model. It contains new analytic results on the moments of phase difference in single look data, and on multi-look distributions of amplitude and phase. As yet no analytic results for the moments of multi-look phase difference have been found, except in limiting cases. The maximum likelihood estimators of the covariance matrix of two jointly Gaussian channels is derived, along with their asymptotic variances. A more complete discussion of these ideas is presented by Tough et al. (1994). >

Miscellanea. A multivariate family of distributions on (0, ∞)p

Abstract: In the context of parametric survival analysis, it is necessary to specify probability distributions on (0, ∞). Typically, the exponential, Weibull, gamma, Pareto or log-normal is used. However, attempts to generalise these distributions to a multivariate setting have proved problematic. This paper introduces a univariate family of distributions, called the beta-log-normal family, motivated by a mixture representation of some of the more typical distributions and which generalises naturally to the multivariate case.

Classes of Ordinary Differential Equations Obtained for the Probability Functions of Gompertz and Gamma Gompertz Distributions

Abstract: In this paper, the differential calculus was used to obtain some classes of ordinary differential equations (ODE) for the probability density function, quantile function, survival function, inverse survival function, hazard function and reversed hazard function of the Gompertz and gamma Gompertz distributions. The stated necessary conditions required for the existence of the ODEs are consistent with the various parameters that defined the distributions. Solutions of these ODEs by using numerous available methods are a new ways of understanding the nature of the probability functions that characterize the distributions. The method can be extended to other probability distributions and can serve an alternative to approximation especially the cases of the quantile and inverse survival functions. Finally, the link between distributions extended to their differential equations as seen in the case of the ODE of the hazard function of the gamma Gompertz and exponential distributions.

Classes of Ordinary Differential Equations Obtained for the Probability Functions of Burr XII and Pareto Distributions

Abstract: In this paper, the differential calculus was used to obtain some classes of ordinary differential equations (ODE) for the probability density function, quantile function, survival function, inverse survival function, hazard function and reversed hazard function of Burr XII and Pareto distributions. This was made easier since later distribution is a special case of the former. The stated necessary conditions required for the existence of the ODEs are consistent with the various parameters that defined the distributions. Solutions of these ODEs by using numerous available methods are new ways of understanding the nature of the probability functions that characterize the distributions.