Goodness-of-fit techniques
Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
A practical guide to heavy tails: statistical techniques and applications
A practical guide to heavy tails: statistical techniques and applications
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Computational Statistics Handbook with MATLAB, Second Edition (Chapman & Hall/Crc Computer Science & Data Analysis)
Maximum likelihood localization of sources in noise modeled as astable process
IEEE Transactions on Signal Processing
An incremental EM-based learning approach for on-line prediction of hospital resource utilization
Artificial Intelligence in Medicine
Multivariate scale mixture of gaussians modeling
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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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 expectation-maximization (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 hydrophone data, to wideband synthetic aperture sonar data, and to multichannel radar sea clutter data.