Shift theorem

The Fréchet distribution is a special case of the generalized extreme value distribution. It has the cumulative distribution function

$\Pr(X\leq x)=e^{-x^{-\alpha }}{\text{ if }}x>0.$

where α > 0 is a shape parameter. It can be generalised to include a location parameter m (the minimum) and a scale parameter s > 0 with the cumulative distribution function

$\Pr(X\leq x)=e^{-\left({\frac {x-m}{s}}\right)^{-\alpha }}{\text{ if }}x>m.$

Named for Maurice Fréchet who wrote a related paper in 1927, further work was done by Fisher and Tippett in 1928 and by Gumbel in 1958.

Characteristics

The single parameter Fréchet with parameter $\alpha$ has standardized moment

$\mu _{k}=\int _{0}^{\infty }x^{k}f(x)dx=\int _{0}^{\infty }t^{-{\frac {k}{\alpha }}}e^{-t}\,dt,$

(with $t=x^{-\alpha }$) defined only for $k<\alpha$:

$\mu _{k}=\Gamma \left(1-{\frac {k}{\alpha }}\right)$

where $\Gamma \left(z\right)$ is the Gamma function.

In particular:

• For $\alpha >1$ the expectation is $E[X]=\Gamma (1-{\tfrac {1}{\alpha }})$
• For $\alpha >2$ the variance is ${\text{Var}}(X)=\Gamma (1-{\tfrac {2}{\alpha }})-{\big (}\Gamma (1-{\tfrac {1}{\alpha }}){\big )}^{2}.$

The quantile $q_{y}$ of order $y$ can be expressed through the inverse of the distribution,

$q_{y}=F^{-1}(y)=\left(-\log _{e}y\right)^{-{\frac {1}{\alpha }}}$.

In particular the median is:

$q_{1/2}=(\log _{e}2)^{-{\frac {1}{\alpha }}}.$

The mode of the distribution is $\left({\frac {\alpha }{\alpha +1}}\right)^{\frac {1}{\alpha }}.$

Especially for the 3-parameter Fréchet, the first quartile is $q_{1}=m+{\frac {s}{\sqrt[{\alpha }]{\log(4)}}}$ and the third quartile $q_{3}=m+{\frac {s}{\sqrt[{\alpha }]{\log({\frac {4}{3}})}}}.$

Also the quantiles for the mean and mode are:

$F(mean)=\exp \left(-\Gamma ^{-\alpha }\left(1-{\frac {1}{\alpha }}\right)\right)$

$F(mode)=\exp \left(-{\frac {\alpha +1}{\alpha }}\right).$

Fitted cumulative Fréchet distribution to extreme one-day rainfalls

Applications

• In hydrology, the Fréchet distribution is applied to extreme events such as annually maximum one-day rainfalls and river discharges. The blue picture illustrates an example of fitting the Fréchet distribution to ranked annually maximum one-day rainfalls in Oman showing also the 90% confidence belt based on the binomial distribution. The cumulative frequencies of the rainfall data are represented by plotting positions as part of the cumulative frequency analysis. However, in most hydrological applications, the distribution fitting is via the generalized extreme value distribution as this avoids imposing the assumption that the distribution does not have a lower bound (as required by the Frechet distribution). Potter or Ceramic Artist Truman Bedell from Rexton, has interests which include ceramics, best property developers in singapore developers in singapore and scrabble. Was especially enthused after visiting Alejandro de Humboldt National Park.

Related distributions

• If $X\sim U(0,1)\,$ (Uniform distribution (continuous)) then $m+s(-\log(X))^{-1/\alpha }\sim {\textrm {Frechet}}(\alpha ,s,m)\,$
• If $X\sim {\textrm {Frechet}}(\alpha ,s,m)\,$ then $kX+b\sim {\textrm {Frechet}}(\alpha ,ks,km+b)\,$
• If $X_{i}={\textrm {Frechet}}(\alpha ,s,m)\,$ and $Y=\max\{\,X_{1},\ldots ,X_{n}\,\}\,$ then $Y\sim {\textrm {Frechet}}(\alpha ,n^{\tfrac {1}{\alpha }}s,m)\,$
• The cumulative distribution function of the Frechet distribution solves the maximum stability postulate equation
• If $X\sim {\textrm {Weibull}}(k=\alpha ,\lambda =m)\,$ (Weibull distribution) then ${\tfrac {m^{2}}{X}}\sim {\textrm {Frechet}}(\alpha ,m)\,$

Properties

• The Frechet distribution is a max stable distribution
• The negative of a random variable having a Frechet distribution is a min stable distribution

See also

• Type-2 Gumbel distribution
• Fisher–Tippett–Gnedenko theorem
• CumFreq (application software for probability distributions including Fréchet)

References

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Publications

• Fréchet, M., (1927). "Sur la loi de probabilité de l'écart maximum." Ann. Soc. Polon. Math. 6, 93.
• Fisher, R.A., Tippett, L.H.C., (1928). "Limiting forms of the frequency distribution of the largest and smallest member of a sample." Proc. Cambridge Philosophical Society 24:180–190.
• Gumbel, E.J. (1958). "Statistics of Extremes." Columbia University Press, New York.
• Kotz, S.; Nadarajah, S. (2000) Extreme value distributions: theory and applications, World Scientific. ISBN 1-86094-224-5

External links

• Bank of England working paper
• An application of a new extreme value distribution to air pollution data
• Wave Analysis for Fatigue and Oceanography

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