Paper Info
Title
On the characterization, generation, and efficient estimation of the complex multivariate GGD
Abstract
The complex multivariate generalized Gaussian distribution (CMGGD) is a flexible parametrized distribution suitable for a variety of applications. Previous work in this area is either limited to the univariate case or, in the multivariate case, restricts the complex vectors, unjustifiably, to be circular. In both cases, algorithms for parameter estimation also suffer from convergence or accuracy limitations over the complete range of their parameters. In this work, we develop the probability density function (PDF) for CMGGD that properly describes noncircular complex data. We then develop a fixed-point algorithm for the estimation of parameters of the CMGGD that is both rapid in its convergence and accurate for the complete shape parameter range. We quantify performance against other algorithms while varying noncircularity, shape parameter and data dimensionality and demonstrate robustness and gains in performance, especially for noncircular data.
Year
DOI
Venue
2016
10.1109/SAM.2016.7569684
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
Keywords
Field
DocType
CMGGD,complex multivariate generalized Gaussian distribution,complex vectors,parameter estimation,convergence,probability density function,PDF,noncircular complex data,fixed-point algorithm,shape parameter,data dimensionality
Random variable,Mathematical optimization,Multivariate kernel density estimation,Joint probability distribution,Computer science,Shape parameter,Estimation theory,Univariate,Statistics,Probability density function,Generalized normal distribution
Conference
ISBN
Citations 
PageRank 
978-1-5090-2104-8
0
0.34
References 
Authors
14
4
Name
Order
Citations
PageRank
Rami Mowakeaa100.34
Zois Boukouvalas2106.27
Tülay Adali31690126.40
Charles Casimiro Cavalcante44514.78