scispace - formally typeset
Search or ask a question
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
More filters
Journal ArticleDOI
TL;DR: In this article, several conditions are established under which a family of elliptical probability density functions possesses a preferable consistency property, which ensures that any marginal distribution of a random vector whose distribution belongs to a specific elliptical family also belongs to the family.

120 citations

Journal ArticleDOI
TL;DR: The statistics of envelope of high-frequency ultrasonic backscatter signals from in vivo normal human dermis and subcutaneous fat were studied and it was found that the Generalized Gamma distribution with two shape parameters provided the best fit among all the distributions in terms of the KS goodness of fit.
Abstract: The statistics of envelope of high-frequency ultrasonic backscatter signals from in vivo normal human dermis and subcutaneous fat were studied. The capability of six probability distributions (Rayleigh, Rician, K, Nakagami, Weibull, and Generalized Gamma) to model empirical envelope data was studied using the Kolmogorov-Smirnov (KS) goodness of fit statistic. The parameters of all the distributions were obtained using the maximum likelihood method. It was found that the Generalized Gamma distribution with two shape parameters provided the best fit among all the distributions in terms of the KS goodness of fit. The K and Weibull distributions also modeled the envelope statistics well. The Rayleigh and Rician distributions provided poorer fits. The parameters of the Generalized Gamma distribution, however, showed a larger variability than those of the other distributions. The intersubject variability in the estimated parameters of all the distributions was found to be comparable to the intrasubject variability. Fat was seen to exhibit significantly more pre-Rayleigh behavior compared to the dermis. The parameters of the Generalized Gamma distribution also showed significant differences between the dermis at the forearm and fingertip regions.

119 citations

Journal ArticleDOI
TL;DR: In this article, a new family of symmetric unimodal distributions on the circle that contains the uniform, von Mises, cardioid, and wrapped Cauchy distributions, among others, as special cases is proposed.
Abstract: We propose a new family of symmetric unimodal distributions on the circle that contains the uniform, von Mises, cardioid, and wrapped Cauchy distributions, among others, as special cases. The basic form of the densities of this family is very simple, although its normalization constant involves an associated Legendre function. The family of distributions can also be derived by conditioning and projecting certain bivariate spherically and elliptically symmetric distributions on to the circle. Trigonometric moments are available, and a measure of variation is discussed. Aspects of maximum likelihood estimation are considered, and likelihood is used to fit the family of distributions to an example set of data. Finally, extension to a family of rotationally symmetric distributions on the sphere is briefly made.

118 citations

Book
05 Dec 1997
TL;DR: Inverse Probability Confidence Limits and Curve Fitting as discussed by the authors, Bartlett S Function Estimating Likelihood Ratios Needed for an Experiment is used to estimate Likelihood Ratio.
Abstract: Preface. 1 Basic Probability Concepts. 2 Some Initial Definitions. 3 Some Results of Specific Distributions. 4 Discrete Distributions and Combinatorials,5.Specific Discrete Distributions. 6 The Normal (or Gaussian) Distribution and Other Continuous Distributions. 7 Generating Functions and Characteristic Functions. 8 The Monte Carlo Method: Computer Simulation of Experiments. 9 Queueing Theory and Other Probability Questions. 10 Two Dimensional and Multi-Dimensional Distributions.,11.The Central Limit Theorem. 12 Inverse Probability Confidence Limits. 13 Methods for Estimating Parameters. Least Squares and Maximum Likelihood. 14 Curve Fitting. 15 Bartlett S Function Estimating Likelihood Ratios Needed for an Experiment. 16 Interpolating Functions and Unfolding Problems. 17 Fitting Data with Correlations and Constraints. 18 Beyond Maximum Likelihood and Least Squares Robust Methods,References

116 citations

Journal ArticleDOI
TL;DR: A generalized Wishart classifier derived from a non-Gaussian model for polarimetric synthetic aperture radar (PolSAR) data is presented and a Bayesian classification scheme is proposed, which can be used in both supervised and unsupervised modes.
Abstract: In this paper, we present a generalized Wishart classifier derived from a non-Gaussian model for polarimetric synthetic aperture radar (PolSAR) data. Our starting point is to demonstrate that the scale mixture of Gaussian (SMoG) distribution model is suitable for modeling PolSAR data. We show that the distribution of the sample covariance matrix for the SMoG model is given as a generalization of the Wishart distribution and present this expression in integral form. We then derive the closed-form solution for one particular SMoG distribution, which is known as the multivariate K-distribution. Based on this new distribution for the sample covariance matrix, termed as the K -Wishart distribution, we propose a Bayesian classification scheme, which can be used in both supervised and unsupervised modes. To demonstrate the effect of including non-Gaussianity, we present a detailed comparison with the standard Wishart classifier using airborne EMISAR data.

114 citations


Network Information
Related Topics (5)
Markov chain
51.9K papers, 1.3M citations
80% related
Estimator
97.3K papers, 2.6M citations
78% related
Iterative method
48.8K papers, 1.2M citations
76% related
Wavelet
78K papers, 1.3M citations
76% related
Robustness (computer science)
94.7K papers, 1.6M citations
73% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20232
20228
20213
20207
201914
201816