Expectation-Maximization algorithm for bi-variate Normal Inverse Gaussian distribution
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 :)
Cite As
Karol Binkowski (2024). Expectation-Maximization algorithm for bi-variate Normal Inverse Gaussian distribution (https://www.mathworks.com/matlabcentral/fileexchange/22058-expectation-maximization-algorithm-for-bi-variate-normal-inverse-gaussian-distribution), MATLAB Central File Exchange. Retrieved .
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Version | Published | Release Notes | |
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1.4.0.0 | Added convergence criteria, storing em estimates after each M-step, skewness and kurtosis check |
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1.3.0.0 | Added: em convergence criteria, storing of estimates after each M-step, skewness and kurtosis check of simulated sample |
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1.2.0.0 | Updated link to invgrnd.m
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1.1.0.0 | P-P plot has been added to check the fit
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1.0.0.0 |