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An EM type algorithm for maximum likelihood estimation of the normal-inverse Gaussian distribution. (English) Zbl 0996.62015

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 difliculties 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.

MSC:

62F10 Point estimation
65C60 Computational problems in statistics (MSC2010)
62P05 Applications of statistics to actuarial sciences and financial mathematics

Software:

bootstrap
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Full Text: DOI

References:

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