. . "1118970041"^^ . . . . . . . . "\u03BE \u2208 R \u2014 shape."@en . . . "for x \u2208 support"@en . . "x \u2208 [ \u03BC \u2212 \u03C3 / \u03BE, +\u221E) when \u03BE > 0,"@en . . . . . "1494479"^^ . . "\u03C3 > 0 \u2014 scale,"@en . . . "and is Euler\u2019s constant."@en . . . . . . "where"@en . "In teoria della probabilit\u00E0 la distribuzione generalizzata dei valori estremi (dall'inglese generalized extreme value distribution, in sigla GEV), o distribuzione di Fisher-Tippett, \u00E8 una famiglia di distribuzioni di probabilit\u00E0 che raccoglie la distribuzione di Fr\u00E9chet, la distribuzione di Weibull e la distribuzione di Gumbel (come caso al limite). Questa famiglia \u00E8 comune nella teoria dei valori estremi, dove descrive il limite dei massimi in una successione di variabili aleatorie indipendenti, secondo il ."@it . . . . . . "."@en . "En probabilit\u00E9 et statistique, la loi d'extr\u00E9mum g\u00E9n\u00E9ralis\u00E9e est une famille de lois de probabilit\u00E9 continues qui servent \u00E0 repr\u00E9senter des ph\u00E9nom\u00E8nes de valeurs extr\u00EAmes (minimum ou maximum). Elle comprend la loi de Gumbel, la loi de Fr\u00E9chet et la loi de Weibull, respectivement lois d'extr\u00E9mum de type I, II et III. Le th\u00E9or\u00E8me de Fisher-Tippett-Gnedenko \u00E9tablit que la loi d'extremum g\u00E9n\u00E9ralis\u00E9e est la distribution limite du maximum (ad\u00E9quatement normalis\u00E9) d'une s\u00E9rie de variables al\u00E9atoires ind\u00E9pendantes de m\u00EAme distribution (iid). La loi d'extr\u00E9mum g\u00E9n\u00E9ralis\u00E9e est connue sous le nom de loi de Fisher-Tippett, d'apr\u00E8s Ronald Fisher et L. H. C. Tippett qui ont \u00E9tudi\u00E9 les trois formes fonctionnelles ci-dessous. Parfois, ce nom signifie plus particuli\u00E8rement le cas de la loi de Gumbel."@fr . "Die verallgemeinerte Extremwertverteilung ist eine stetige Wahrscheinlichkeitsverteilung. Sie spielt eine herausragende Rolle in der Extremwerttheorie, da sie alle m\u00F6glichen asymptotischen Verteilungen des Maximums einer einfachen Zufallsstichprobe in einer Darstellung zusammenfasst.Die verallgemeinerte Extremwertverteilung fasst die Gumbel-Verteilung, die Fr\u00E9chet-Verteilung und die Weibull-Verteilung zusammen."@de . "\u03BC \u2208 R \u2014 location,"@en . "\u6975\u5024\u5206\u5E03"@ja . "where is the sign function"@en . . . "Generalized extreme value distribution"@en . . . . . "x \u2208"@en . . . . "21374"^^ . . . . . . . . . . . . . . . . "En probabilit\u00E9 et statistique, la loi d'extr\u00E9mum g\u00E9n\u00E9ralis\u00E9e est une famille de lois de probabilit\u00E9 continues qui servent \u00E0 repr\u00E9senter des ph\u00E9nom\u00E8nes de valeurs extr\u00EAmes (minimum ou maximum). Elle comprend la loi de Gumbel, la loi de Fr\u00E9chet et la loi de Weibull, respectivement lois d'extr\u00E9mum de type I, II et III. Le th\u00E9or\u00E8me de Fisher-Tippett-Gnedenko \u00E9tablit que la loi d'extremum g\u00E9n\u00E9ralis\u00E9e est la distribution limite du maximum (ad\u00E9quatement normalis\u00E9) d'une s\u00E9rie de variables al\u00E9atoires ind\u00E9pendantes de m\u00EAme distribution (iid)."@fr . . . . "and is the Riemann zeta function"@en . . . "density"@en . "Die verallgemeinerte Extremwertverteilung ist eine stetige Wahrscheinlichkeitsverteilung. Sie spielt eine herausragende Rolle in der Extremwerttheorie, da sie alle m\u00F6glichen asymptotischen Verteilungen des Maximums einer einfachen Zufallsstichprobe in einer Darstellung zusammenfasst.Die verallgemeinerte Extremwertverteilung fasst die Gumbel-Verteilung, die Fr\u00E9chet-Verteilung und die Weibull-Verteilung zusammen."@de . . "where gk = \u0393,"@en . . . . "Distribuzione generalizzata dei valori estremi"@it . . "In teoria della probabilit\u00E0 la distribuzione generalizzata dei valori estremi (dall'inglese generalized extreme value distribution, in sigla GEV), o distribuzione di Fisher-Tippett, \u00E8 una famiglia di distribuzioni di probabilit\u00E0 che raccoglie la distribuzione di Fr\u00E9chet, la distribuzione di Weibull e la distribuzione di Gumbel (come caso al limite). Questa famiglia \u00E8 comune nella teoria dei valori estremi, dove descrive il limite dei massimi in una successione di variabili aleatorie indipendenti, secondo il . Il secondo nome con cui \u00E8 conosciuta deriva dagli statistici britannici Fisher e Tippett."@it . "\u6975\u5024\u5206\u5E03\uFF08\u304D\u3087\u304F\u3061\u3076\u3093\u3077\u3001\u82F1: extreme value distribution\uFF09\u3068\u306F\u3001\u78BA\u7387\u8AD6\u304A\u3088\u3073\u7D71\u8A08\u5B66\u306B\u304A\u3044\u3066\u3001\u3042\u308B\u7D2F\u7A4D\u5206\u5E03\u95A2\u6570\u306B\u3057\u305F\u304C\u3063\u3066\u751F\u3058\u305F\u5927\u304D\u3055 n \u306E\u6A19\u672C X1,X2, \u2026, Xn \u306E\u3046\u3061\u3001x \u4EE5\u4E0A (\u3042\u308B\u3044\u306F\u4EE5\u4E0B) \u3068\u306A\u308B\u3082\u306E\u306E\u500B\u6570\u304C\u3069\u306E\u3088\u3046\u306B\u5206\u5E03\u3059\u308B\u304B\u3092\u8868\u3059\u3001\u9023\u7D9A\u78BA\u7387\u5206\u5E03\u30E2\u30C7\u30EB\u3067\u3042\u308B\u3002\u7279\u306B\u6700\u5927\u5024\u3084\u6700\u5C0F\u5024\u306A\u3069\u304C\u6F38\u8FD1\u7684\u306B\u5F93\u3046\u5206\u5E03\u3067\u3042\u308A\u3001\u6CB3\u5DDD\u306E\u6C3E\u6FEB\u3001\u6700\u5927\u98A8\u901F\u3001\u6700\u5927\u964D\u96E8\u91CF\u3001\u91D1\u878D\u306B\u304A\u3051\u308B\u30EA\u30B9\u30AF\u7B49\u306E\u5206\u5E03\u306B\u9069\u7528\u3055\u308C\u308B\u3002"@ja . "Extremwertverteilung"@de . . "Loi d'extremum g\u00E9n\u00E9ralis\u00E9e"@fr . . . . . . "In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fr\u00E9chet and Weibull families also known as type I, II and III extreme value distributions. By the extreme value theorem the GEV distribution is the only possible limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables. Note that a limit distribution needs to exist, which requires regularity conditions on the tail of the distribution. Despite this, the GEV distribution is often used as an approximation to model the maxima of long (finite) sequences of random variables."@en . . "\u6975\u5024\u5206\u5E03\uFF08\u304D\u3087\u304F\u3061\u3076\u3093\u3077\u3001\u82F1: extreme value distribution\uFF09\u3068\u306F\u3001\u78BA\u7387\u8AD6\u304A\u3088\u3073\u7D71\u8A08\u5B66\u306B\u304A\u3044\u3066\u3001\u3042\u308B\u7D2F\u7A4D\u5206\u5E03\u95A2\u6570\u306B\u3057\u305F\u304C\u3063\u3066\u751F\u3058\u305F\u5927\u304D\u3055 n \u306E\u6A19\u672C X1,X2, \u2026, Xn \u306E\u3046\u3061\u3001x \u4EE5\u4E0A (\u3042\u308B\u3044\u306F\u4EE5\u4E0B) \u3068\u306A\u308B\u3082\u306E\u306E\u500B\u6570\u304C\u3069\u306E\u3088\u3046\u306B\u5206\u5E03\u3059\u308B\u304B\u3092\u8868\u3059\u3001\u9023\u7D9A\u78BA\u7387\u5206\u5E03\u30E2\u30C7\u30EB\u3067\u3042\u308B\u3002\u7279\u306B\u6700\u5927\u5024\u3084\u6700\u5C0F\u5024\u306A\u3069\u304C\u6F38\u8FD1\u7684\u306B\u5F93\u3046\u5206\u5E03\u3067\u3042\u308A\u3001\u6CB3\u5DDD\u306E\u6C3E\u6FEB\u3001\u6700\u5927\u98A8\u901F\u3001\u6700\u5927\u964D\u96E8\u91CF\u3001\u91D1\u878D\u306B\u304A\u3051\u308B\u30EA\u30B9\u30AF\u7B49\u306E\u5206\u5E03\u306B\u9069\u7528\u3055\u308C\u308B\u3002"@ja . . . . . . . . . . . "x \u2208 when \u03BE = 0,"@en . . . . . . . . "In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fr\u00E9chet and Weibull families also known as type I, II and III extreme value distributions. By the extreme value theorem the GEV distribution is the only possible limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables. Note that a limit distribution needs to exist, which requires regularity conditions on the tail of the distribution. Despite this, the GEV distribution is often used as an approximation to model the maxima of long (finite) sequences of random variables. In some fields of application the generalized extreme value distribution is known as the Fisher\u2013Tippett distribution, named after Ronald Fisher and L. H. C. Tippett who recognised three different forms outlined below. However usage of this name is sometimes restricted to mean the special case of the Gumbel distribution. The origin of the common functional form for all 3 distributions dates back to at least Jenkinson, A. F. (1955), though allegedly it could also have been given by von Mises, R. (1936)."@en . . . .