K-distribution

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

Geoacoustic Inversion Using Physical–Statistical Bottom Reverberation Model in the Deep Ocean

Abstract: Reverberation is an important ocean phenomenon that involves bottom properties. This paper proposes a method of geoacoustic inversion using bottom reverberation in the deep ocean. Models for the probability density function, including Rayleigh, Lognormal normal, K, and Weibull distributions, are commonly used to describe the envelope of bottom reverberation. Based on the measured data in South China Sea, the envelope of bottom reverberation in the experiment is consistent with K distribution. A double exponential distribution is used to generate the amplitude of reverberation, of which the envelope obeys the K distribution. In addition, the propagation and scattering models are introduced as the parameters of double exponential function, wherein the model has a physical mechanism. Therefore, a bottom reverberation model with both physical and statistical characteristics is suitable for geoacoustic inversion. The cost function is used to minimize the mean square difference between the measured and modeled envelope of the bottom reverberation. The inversion results are consistent with those from previous research.

Parametric Methods for Comparing Two Survival Distributions

Abstract: For each case in the sample, we define three variables, Time, Status and Treat. Let Time denote the survival time (exact or censored), Status be a dummy variable with Status=0 if Time is censored and 1 otherwise and Treat be a variable with Treat = MP if the patient received 6-MP and P if the patient receive Placebo. Assume that the data have been saved in “C:\Example.dat” as a text file, which contains three columns (Time, in the first column, Status in the second column, and Treat in the third column), separated by space(s).

Image change detection by possibility distribution dissemblance

Abstract: In this paper we present a new similarity measure between possibility distributions based on the Kullback-Leibler (KL) divergence in the domain of real numbers. The possibility distributions are obtained thanks to the DFMP probability-possibility transformation [1] lying on the principle that a possibility measure can encode a family of probability measures. We consider here two particular possibility distributions built from parameter estimation of the Weibull and Rayleigh probability laws. The analytical expression of the KL divergence for the two considered possibility distributions are given, allowing a simple computation which depends on the parameters of the possibility distribution obtained. This new similarity measure is compared to the existing KL divergence for probability distributions in a context of change detection over simulated images as they provide a ground-truth of the changes required to evaluate the rate of true detection against false alarm.

Informative Quantile Functions and Identification of Probability Distribution Types.

Abstract: : A problem of great importance to statistical data analysts is quick identification of possible probability distributions for observed data, and classification of tail behavior of probability distributions. This paper discusses the informative quantile function IQ(u) = (Q(u) - Q(0.5)) divided by 2(Q(0.75) - Q(0.25)), and its use to identify probability models for observed data and its use to provide concepts of representative distributions which illustrate the different types of shapes and tail behavior that real distributions can have. This paper also discusses estimators of tail exponents; they can be used to identify outlying data values, and more centrally to identify possible distributions to fit to data. (Author)