# Fisher–Tippett–Gnedenko theorem In statistics, the Fisher–Tippett–Gnedenko theorem (also the Fisher–Tippett theorem or the extreme value theorem) is a general result in extreme value theory regarding asymptotic distribution of extreme order statistics. The maximum of a sample of iid random variables after proper renormalization can only converge in distribution to one of 3 possible distributions, the Gumbel distribution, the Fréchet distribution, or the Weibull distribution. Credit for the extreme value theorem and its convergence details are given to Fréchet (1927), Ronald Fisher and Leonard Henry Caleb Tippett (1928), Mises (1936) and Gnedenko (1943). The role of the extremal types theorem for maxima is similar to that of central limit theorem for averages, except that the central limit theorem applies to the average of a sample from any distribution with finite variance, while the Fisher–Tippet–Gnedenko theorem only states that if the distribution of a normalized maximum converges, then the limit has to be one of a particular class of distributions. It does not state that the distribution of the normalized maximum does converge.

Contents 1 Statement 2 Conditions of convergence 3 Examples 3.1 Fréchet distribution 3.2 Gumbel distribution 3.3 Weibull distribution 4 See also 5 Notes Statement Let {displaystyle X_{1},X_{2},ldots ,X_{n}} be a sequence of independent and identically-distributed random variables with cumulative distribution function {displaystyle F} . Suppose that there exist two sequences of real numbers {displaystyle a_{n}>0} and {displaystyle b_{n}in mathbb {R} } such that the following limits converge to a non-degenerate distribution function: {displaystyle lim _{nto infty }Pleft({frac {max{X_{1},dots ,X_{n}}-b_{n}}{a_{n}}}leq xright)=G(x)} , or equivalently: {displaystyle lim _{nto infty }F^{n}left(a_{n}x+b_{n}right)=G(x)} .

In such circumstances, the limit distribution {displaystyle G} belongs to either the Gumbel, the Fréchet or the Weibull family. In other words, if the limit above converges we will have {displaystyle G(x)} assume the form: {displaystyle G_{gamma }(x)=exp left(-(1+gamma ,x)^{-1/gamma }right),;;1+gamma ,x_{a,b}>0} or else {displaystyle G_{0}(x)=exp left(-exp(-x)right)} for some parameter {displaystyle gamma .} This is the cumulative distribution function of the generalized extreme value distribution (GEV) with extreme value index {displaystyle gamma } . The GEV distribution groups the Gumbel, Fréchet and Weibull distributions into a single one. Note that the second formula (the Gumbel distribution) is the limit of the first as {displaystyle gamma } goes to zero.

Conditions of convergence The Fisher–Tippett–Gnedenko theorem is a statement about the convergence of the limiting distribution {displaystyle G(x)} above. The study of conditions for convergence of {displaystyle G} to particular cases of the generalized extreme value distribution began with Mises, R. (1936) and was further developed by Gnedenko, B. V. (1943). Let {displaystyle F} be the distribution function of {displaystyle X} , and {displaystyle X_{1},dots ,X_{n}} an i.i.d. sample thereof. Also let {displaystyle x^{*}} be the populational maximum, i.e. {displaystyle x^{*}=sup{xmid F(x)<1}} . The limiting distribution of the normalized sample maximum, given by {displaystyle G} above, will then be: A Fréchet distribution ( {displaystyle gamma >0} ) if and only if {displaystyle x^{*}=infty } and {displaystyle lim _{trightarrow infty }{frac {1-F(ut)}{1-F(t)}}=u^{-1/|gamma |}} for all {displaystyle u>0} . This corresponds to what we may call a heavy tail. In this case, possible sequences that will satisfy the theorem conditions are {displaystyle b_{n}=0} and {displaystyle a_{n}=F^{-1}left(1-{frac {1}{n}}right)} . A Gumbel distribution ( {displaystyle gamma =0} ), with {displaystyle x^{*}} finite or infinite, if and only if {displaystyle lim _{trightarrow x^{*}}{frac {1-F(t+uf(t))}{1-F(t)}}=e^{-u}} for all {displaystyle u>0} with {displaystyle f(t):={frac {int _{t}^{x^{*}}1-F(s)ds}{1-F(t)}}} . Possible sequences here are {displaystyle b_{n}=F^{-1}left(1-{frac {1}{n}}right)} and {displaystyle a_{n}=fleft(F^{-1}left(1-{frac {1}{n}}right)right)} . A Weibull distribution ( {displaystyle gamma <0} ) if and only if {displaystyle x^{*}} is finite and {displaystyle lim _{trightarrow 0^{+}}{frac {1-F(x^{*}-ut)}{1-F(x^{*}-t)}}=u^{1/|gamma |}} for all {displaystyle u>0} . Possible sequences here are {displaystyle b_{n}=x^{*}} and {displaystyle a_{n}=x^{*}-F^{-1}left(1-{frac {1}{n}}right)} . Examples Fréchet distribution If we take the Cauchy distribution {displaystyle f(x)=(pi ^{2}+x^{2})^{-1}} the cumulative distribution function is: {displaystyle F(x)=1/2+{frac {1}{pi }}arctan(x/pi )} {displaystyle 1-F(x)} is asymptotic to {displaystyle 1/x,} or {displaystyle ln F(x)sim -1/x} and we have {displaystyle ln F(x)^{n}=nln F(x)sim -n/x.} Thus we have {displaystyle F(x)^{n}approx exp(-n/x)} and letting {displaystyle u=x/n-1} (and skipping some explanation) {displaystyle lim _{nto infty }F(nu+n)^{n}=exp(-(1+u)^{-1})=G_{1}(u)} for any {displaystyle u.} The expected maximum value therefore goes up linearly with n.

Gumbel distribution Let us take the normal distribution with cumulative distribution function {displaystyle F(x)={frac {1}{2}}{text{erfc}}(-x/{sqrt {2}}).} We have {displaystyle ln F(x)sim -{frac {exp(-x^{2}/2)}{{sqrt {2pi }}x}}} and {displaystyle ln F(x)^{n}=nln F(x)sim -{frac {nexp(-x^{2}/2)}{{sqrt {2pi }}x}}.} Thus we have {displaystyle F(x)^{n}approx exp left(-{frac {nexp(-x^{2}/2)}{{sqrt {2pi }}x}}right).} If we define {displaystyle c_{n}} as the value that satisfies {displaystyle {frac {nexp(-c_{n}^{2}/2)}{{sqrt {2pi }}c_{n}}}=1} then around {displaystyle x=c_{n}} {displaystyle {frac {nexp(-x^{2}/2)}{{sqrt {2pi }}x}}approx exp(c_{n}(c_{n}-x)).} As n increases, this becomes a good approximation for a wider and wider range of {displaystyle c_{n}(c_{n}-x)} so letting {displaystyle u=c_{n}(c_{n}-x)} we find that {displaystyle lim _{nto infty }F(u/c_{n}+c_{n})^{n}=exp(-exp(-u))=G_{0}(u).} Equivalently, {displaystyle lim _{nto infty }P{Bigl (}{frac {max{X_{1},ldots ,X_{n}}-c_{n}}{1/c_{n}}}leq u{Bigr )}=exp(-exp(-u))=G_{0}(u).} We can see that {displaystyle ln c_{n}sim (ln ln n)/2} and then {displaystyle c_{n}sim {sqrt {2ln n}}} so the maximum is expected to climb ever more slowly toward infinity.

Weibull distribution We may take the simplest example, a uniform distribution between 0 and 1, with cumulative distribution function {displaystyle F(x)=x} from 0 to 1.

Approaching 1 we have {displaystyle ln F(x)^{n}=nln F(x)sim -n(1-x).} Then {displaystyle F(x)^{n}approx exp(nx-n).} Letting {displaystyle u=1+n(1-x)} we have {displaystyle lim _{nto infty }F(u/n+1)^{n}=exp left(-(1-u)right)=G_{-1}(u).} The expected maximum approaches 1 inversely proportionally to n.

See also Extreme value theory Gumbel distribution Generalized extreme value distribution Pickands–Balkema–de Haan theorem Generalized Pareto distribution Exponentiated generalized Pareto distribution Notes ^ Fréchet, M. (1927), "Sur la loi de probabilité de l'écart maximum", Annales de la Société Polonaise de Mathématique, 6 (1): 93–116 ^ Fisher, R.A.; Tippett, L.H.C. (1928), "Limiting forms of the frequency distribution of the largest and smallest member of a sample", Proc. Camb. Phil. Soc., 24 (2): 180–190, Bibcode:1928PCPS...24..180F, doi:10.1017/s0305004100015681 ^ Jump up to: a b Mises, R. von (1936). "La distribution de la plus grande de n valeurs". Rev. Math. Union Interbalcanique 1: 141–160. ^ Jump up to: a b Falk, Michael; Marohn, Frank (1993). "Von Mises conditions revisited". The Annals of Probability: 1310–1328. ^ Jump up to: a b c Gnedenko, B.V. (1943), "Sur la distribution limite du terme maximum d'une serie aleatoire", Annals of Mathematics, 44 (3): 423–453, doi:10.2307/1968974, JSTOR 1968974 ^ Mood, A.M. (1950). "5. Order Statistics". Introduction to the theory of statistics. New York, NY, US: McGraw-Hill. pp. 251–270. ^ Jump up to: a b Haan, Laurens; Ferreira, Ana (2007). Extreme value theory: an introduction. Springer. Categories: Theorems in statisticsExtreme value dataTails of probability distributions

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