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Type-2 Gumbel distribution

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Probability distribution
Type-2 Gumbel
Parameters   a R   {\displaystyle \ a\in \mathbb {R} \ } (shape),
  b R   {\displaystyle \ b\in \mathbb {R} \ } (scale)
Support   0 < x <   {\displaystyle \ 0<x<\infty \ }
PDF   a   b   x a 1   e b   x a   {\displaystyle \ a\ b\ x^{-a-1}\ e^{-b\ x^{-a}}\ }
CDF   e b   x a   {\displaystyle \ e^{-b\ x^{-a}}\ }
Quantile   (     log e ( p )   b ) 1 a   {\displaystyle \ \left(-\ {\frac {\ \log _{e}\!\left(p\right)\ }{b}}\right)^{-{\frac {1}{a}}}\ }
Mean   b 1 a   Γ (   1   1   a   )   {\displaystyle \ b^{\frac {1}{a}}\ \Gamma \!\left(\ 1-{\tfrac {\ 1\ }{a}}\ \right)\ }
Variance   b 2 a   Γ ( 1   1   a   ) ( 1 Γ ( 1 1 a ) )   {\displaystyle \ b^{\frac {2}{a}}\ \Gamma \!\left(1-{\tfrac {\ 1\ }{a}}\ \right){\Bigl (}1-\Gamma \!\left(1-{\tfrac {1}{a}}\right){\Bigr )}\ }

In probability theory, the Type-2 Gumbel probability density function is

  f ( x | a , b ) = a   b   x a 1   e b   x a {\displaystyle \ f(x|a,b)=a\ b\ x^{-a-1}\ e^{-b\ x^{-a}}\quad } for x > 0   . {\displaystyle \quad x>0~.}

For   0 < a 1   {\displaystyle \ 0<a\leq 1\ } the mean is infinite. For   0 < a 2   {\displaystyle \ 0<a\leq 2\ } the variance is infinite.

The cumulative distribution function is

  F ( x | a , b ) = e b   x a   . {\displaystyle \ F(x|a,b)=e^{-b\ x^{-a}}~.}

The moments   E [ X k ]   {\displaystyle \ \mathbb {E} {\bigl }\ } exist for   k < a   {\displaystyle \ k<a\ }

The distribution is named after Emil Julius Gumbel (1891 – 1966).

Generating random variates

Given a random variate   U   {\displaystyle \ U\ } drawn from the uniform distribution in the interval   ( 0 , 1 )   , {\displaystyle \ (0,1)\ ,} then the variate

X = ( ln U b ) 1 a   {\displaystyle X=\left(-{\frac {\ln U}{b}}\right)^{-{\frac {1}{a}}}\ }

has a Type-2 Gumbel distribution with parameter   a   {\displaystyle \ a\ } and   b   . {\displaystyle \ b~.} This is obtained by applying the inverse transform sampling-method.

Related distributions

  • Substituting   b = λ k   {\displaystyle \ b=\lambda ^{-k}\ } and   a = k   {\displaystyle \ a=-k\ } yields the Weibull distribution. Note, however, that a positive   k   {\displaystyle \ k\ } (as in the Weibull distribution) would yield a negative   a   {\displaystyle \ a\ } and hence a negative probability density, which is not allowed.

Based on "Gumbel distribution". The GNU Scientific Library. type 002d2, used under GFDL.

See also

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