Generalized inverse Gaussian distribution

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Generalized inverse Gaussian
Probability density function
Cumulative distribution function
Parameters a>0,b>0,p
Support x>0
Probability density function (pdf) f(x) = \frac{(a/b)^{p/2}}{2 K_p(\sqrt{ab})} x^{(p-1)} e^{-(ax + b/x)/2}
Cumulative distribution function (cdf)
Mean \frac{\sqrt{b}\ K_{-1-p}(\sqrt{a b}) }{ \sqrt{a}\ K_{p}(\sqrt{a b})}
Median
Mode
Variance
Skewness
Excess kurtosis
Entropy
Moment-generating function (mgf)
Characteristic function


In probability theory, the generalized inverse Gaussian distribution (GIG) is a continuous probability distribution with probability density function

f(x) = \frac{(a/b)^{p/2}}{2 K_p(\sqrt{ab})} x^{(p-1)} e^{-(ax + b/x)/2},

where x > 0, Kp is a modified Bessel function of the third kind, a > 0, and b > 0. It is used extensively in geostatistics, statistical linguistics, finance, etc. This distribution was first proposed by Etienne Halphen[1] It was rediscovered and popularised by Ole Barndorff-Nielsen, who called it the generalized inverse Gaussian distribution, and Herbert Sichel. It is also known as the Sichel distribution.

[edit] Notes

  1. ^ V. Seshadri (1997): Halphen's laws. In S. Kotz, C. B. Read and D. L. Banks (eds.): Encyclopedia of Statistical Sciences, Update Volume 1, pp. 302 - 306. Wiley, New York.


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