An EM type algorithm for maximum likelihood estimation of the normal-inverse Gaussian distribution
The Normal-Inverse Gaussian distribution arises as a Normal variance-mean mixture with an Inverse Gaussian mixing distribution. This article deals with Maximum Likelihood estimation of the parameters of the Normal-Inverse Gaussian distribution. Due to the complexity of the likelihood, direct maximization is difficult. An EM type algorithm is provided for the Maximum Likelihood estimation of the Normal-Inverse Gaussian distribution. This algorithm overcomes numerical difficulties occurring when standard numerical techniques are used. An application to a data set concerning the general index of the Athens Stock Exchange is given. Some operating characteristics of the algorithm are discussed.
Volume (Year): 57 (2002)
Issue (Month): 1 (March)
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