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

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https://hdl.handle.net/10037/18751
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Date
2004-09
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Øigård, Tor Arne; Hanssen, Alfred; Hansen, Roy Edgar
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.
Description
Source at https://www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/html/abstract/a1534.htm
Publisher
European Association for Signal Processing
Citation
Øigård, T.A., Hanssen, A. & Hansen, R.E. (2004). The Multivariate Normal Inverse Gaussian distribution: EM-estimation and analysis of synthetic aperture sonar data. Proceedings of the 12th European Signal Processing Conference., 06-10 September 2004, Vienna, Austria, 1433-1436.
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Copyright 2004 European Association for Signal Processing

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