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.
Let be a sequence of independent and identically-distributed random variables with cumulative distribution function . Suppose that there exist two sequences of real numbers and such that the following limits converge to a non-degenerate distribution function:
In other words, if the limit above converges we will have assume the form:
for some parameters . Remarkably, the right hand side is the cumulative distribution function of the generalized extreme value distribution (GEV) with extreme value index , scale parameter and location parameter . The GEV distribution groups the Gumbel, Fréchet and Weibull distributions into a single one.
Conditions of convergence
The Fisher–Tippett–Gnedenko theorem is a statement about the convergence of the limiting distribution above. The study of conditions for convergence of to particular cases of the generalized extreme value distribution began with Mises, R. (1936) and was further developed by Gnedenko, B. V. (1943).
Let be the distribution function of , and an i.i.d. sample thereof. Also let be the populational maximum, i.e. . The limiting distribution of the normalized sample maximum, given by above, will then be:
- A Fréchet distribution () if and only if and for all .
- In this case, possible sequences that will satisfy the theorem conditions are and .
- A Weibull distribution () if and only if is finite and for all .
- Possible sequences here are and .
- A Gumbel distribution () if and only if with .
- Possible sequences here are and .
- Extreme value theory
- Gumbel distribution
- Generalized extreme value distribution
- Pickands–Balkema–de Haan theorem
- Generalized Pareto distribution
- Exponentiated generalized Pareto distribution
- 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
- Mises, R. von (1936). "La distribution de la plus grande de n valeurs". Rev. Math. Union Interbalcanique 1: 141–160.
- Falk, Michael; Marohn, Frank (1993). "Von Mises conditions revisited". The Annals of Probability: 1310–1328.
- 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.
- Haan, Laurens; Ferreira, Ana (2007). Extreme value theory: an introduction. Springer.