Expectation-Maximization algorithm for bi-variate Normal Inverse Gaussian distribution

Version 1.4.0.0 (7,28 ko) par Karol Binkowski
Expectation-Maximization (EM) algorithm for bi-variate Normal Inverse Gaussian (biNIG) distribution
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Mise à jour 27 juil. 2009

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EM estimation of parameters of bi variate NIG distribution.

The test file:
1. Simulate biNIG sample with use of randraw.m (http://www.mathworks.com/matlabcentral/fileexchange/7309)
or invgrnd.m (http://www.mathworks.com/matlabcentral/fileexchange/10934) .
2. Calls EMBIVNIG.m (values of starting parameters are chosen arbitrary).
3. Calls binigpp.m for P-P plot to check the fit.

References:

"EM-estimation and modeling of heavy-tailed processes with the multivariate normal inverse Gaussian distribution", Oigard, Hanssen, Hansen and Godtliebsen, Signal Processing, vol. 85 (2005), p. 1655-1673

"The Two-Dimensional Hyperbolic Distribution and Related Distributions, with an Application to Johannsen's Bean Data", P. Blaesild, Biometrika, vol. 68, No. 1 (Apr., 1981), pp. 251-263, (Theorem 1 (a) & (c), p. 253)

Any comments welcome :)

Citation pour cette source

Karol Binkowski (2026). Expectation-Maximization algorithm for bi-variate Normal Inverse Gaussian distribution (https://fr.mathworks.com/matlabcentral/fileexchange/22058-expectation-maximization-algorithm-for-bi-variate-normal-inverse-gaussian-distribution), MATLAB Central File Exchange. Extrait(e) le .

Compatibilité avec les versions de MATLAB
Créé avec R2006b
Compatible avec toutes les versions
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Version Publié le Notes de version
1.4.0.0

Added convergence criteria, storing em estimates after each M-step, skewness and kurtosis check
1.3.0.0

Added: em convergence criteria, storing of estimates after each M-step, skewness and kurtosis check of simulated sample
1.2.0.0

Updated link to invgrnd.m
(http://www.mathworks.com/matlabcentral/fileexchange/10934)
1.1.0.0

P-P plot has been added to check the fit
(added files: binigpp.m, binigm1.m, binigm2.m, nigcdfb.m, nigpdfb.m).
1.0.0.0