The Multivariate Normal Inverse Gaussian distribution: EM-estimation and analysis of synthetic aperture sonar data

Author
Øigård, Tor Arne
Hanssen, Alfred
Hansen, Roy Edgar
Date Issued
2015
Permalink
http://hdl.handle.net/20.500.12242/658
https://publications.ffi.no/123456789/658
Collection
Articles
Description
Øigård, Tor Arne; Hanssen, Alfred; Hansen, Roy Edgar. The Multivariate Normal Inverse Gaussian distribution: EM-estimation and analysis of synthetic aperture sonar data. European Signal Processing Conference 2015 ;Volum 06-10-September-2004. s. 1433-1436
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Abstract
The heavy-tailed Multivariate Normal Inverse Gaussian (MNIG) distribution is a recent variance-mean mixture of a multivariate Gaussian with a univariate inverse Gaussian distribution. Due to the complexity of the likelihood function, parameter estimation by direct maximization is exceedingly difficult. To overcome this problem, we propose a fast and accurate multivariate ExpectationMaximization (EM) algorithm for maximum likelihood estimation of the scalar, vector, and matrix parameters of the MNIG distribution. Important fundamental and attractive properties of the MNIG as a modeling tool for multivariate heavy-tailed processes are discussed. The modeling strength of the MNIG, and the feasibility of the proposed EM parameter estimation algorithm, are demonstrated by fitting the MNIG to real world wideband synthetic aperture sonar data.
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