Extreme value theory

Extreme value theory studies limiting probability distributions of normalized sample maxima and related extremes. The standard limits for maxima include the Fréchet distribution, negative Weibull distribution, and Gumbel distribution.

The standard Gumbel distribution for maxima has distribution function $\Lambda (x)=e^{-e^{-x}}$ for every real $x$. The maximum domain of attraction is characterized by an exponential tail ratio on the scale of a Gumbel auxiliary function.

For the Gamma distribution with fixed positive integer shape $m$ and rate one, the survival function is $e^{-t}{\sum }_{j=0}^{m-1}{t}^j/j!$. Its sample maximum centered by $\log n+(m-1)\log \log n-\log ((m-1)!)$ has convergence in distribution to the standard Gumbel distribution, without an additional scale factor.

A positive Gumbel auxiliary function makes $\overline{F}(t+a(t)x)/\overline{F}(t)\to e^{-x}$ as $t$ approaches the right endpoint of a distribution, for every real $x$. Under the Von Mises conditions for extreme values, the reciprocal hazard function is a valid choice.

For $\alpha >0$, the standard negative Weibull distribution has distribution function ${\Psi }_{\alpha }(x)=e^{-(-x{)}^{\alpha }}$ for $x<0$ and one for $x\ge 0$. This is the probability distribution of the negative of a standard positive Weibull distribution variable.

For $\alpha >0$, the standard Fréchet distribution has distribution function ${\Phi }_{\alpha }(x)=e^{-x^{-\alpha }}$ for $x>0$ and zero for $x\le 0$. Its maximum domain of attraction consists of distribution functions with an infinite right endpoint of a distribution and a survival function with regular variation of index $-\alpha$.

A distribution function $F$ lies in the maximum domain of attraction of $G$ if $F(a_{n}x+b_n{)}^n\to G(x)$ at every continuity point of $G$ for some $a_n>0$ and real $b_n$. This expresses convergence in distribution of normalized sample maxima.

For an absolutely continuous distribution function with positive probability density function near its right endpoint of a distribution, write $\eta =f/(1-F)$ for its hazard function. The limits $t\eta (t)\to \alpha$ at an infinite endpoint or $(x_F-t)\eta (t)\to \alpha$ at a finite endpoint imply attraction to the Fréchet distribution or negative Weibull distribution, respectively. A continuously differentiable reciprocal hazard function with $(1/\eta {)}^{\prime }\to 0$ implies attraction to the Gumbel distribution, provided the survival function tends to zero at the endpoint.