Limitations of the Normal-inverse Gaussian Distribution with respect to Skewness-Kurtosis Associations
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Beschreibung
This thesis provides an analysis regarding the limitations of the normal-inverse Gaussian distribution with respect to Skewness-Kurtosis Associations.
This thesis provides an analysis regarding the limitations of the normal-inverse Gaussian distribution with respect to Skewness-Kurtosis Associations.
The normal-inverse Gaussian distribution, which is absolutely continuously supported on the entire real line, represents a highly versatile type of distribution, where its four parameters can be identified in a straightforward analytical manner from (some prescribed) mean, variance, skewness and excess kurtosis.
However, despite its great adaptability, certain skewness-kurtosis associations cannot be covered by this type of distribution.
This skewness-kurtosis relationship will be discussed below following a brief introduction to the distribution type itself.
That, in a certain way "blind spot", will subsequently be evaluated by means of comparable distribution types, which are absolutely continuously supported on the entire real line as well.
The methodology will be presented formally by means of the actual source code implemented for this purpose, using the programming language R.
Autor*in
Johann Markus Schauerhuber
During his studies and academic resp. professional activities Prof. Dr. Dr. Johann Markus Schauerhuber has been intensively involved in statistical programming, stochastic, risk theory, simulation and mathematical modeling.
Most of his professional experience has been gained in the university domain as an academic director, postdoc lecturer / researcher and in government authorities.
Email: jm_schauerhuber@gmx.at
Themen in »Limitations of the Normal-inverse Gaussian Distribution with respect to Skewness-Kurtosis Associations«
Normal-inverse Gaussian Distribution Skewness Kurtosis