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  2. Extreme value theorem - Wikipedia

    en.wikipedia.org/wiki/Extreme_value_theorem

    The extreme value theorem was originally proven by Bernard Bolzano in the 1830s in a work Function Theory but the work remained unpublished until 1930. Bolzano's proof consisted of showing that a continuous function on a closed interval was bounded, and then showing that the function attained a maximum and a minimum value.

  3. Gumbel distribution - Wikipedia

    en.wikipedia.org/wiki/Gumbel_distribution

    The standard Gumbel distribution is the case where = and = with cumulative distribution function = ()and probability density function = (+).In this case the mode is 0, the median is ⁡ (⁡ ()), the mean is (the Euler–Mascheroni constant), and the standard deviation is /

  4. Generalized extreme value distribution - Wikipedia

    en.wikipedia.org/wiki/Generalized_extreme_value...

    In probability theory and statistics, the generalized extreme value (GEV) distribution [2] is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions. By the extreme value theorem the GEV distribution ...

  5. Extreme value theory - Wikipedia

    en.wikipedia.org/wiki/Extreme_value_theory

    Extreme value theory or extreme value analysis (EVA) is the study of extremes in statistical distributions. It is widely used in many disciplines, such as structural engineering , finance , economics , earth sciences , traffic prediction, and geological engineering .

  6. Fréchet distribution - Wikipedia

    en.wikipedia.org/wiki/Fréchet_distribution

    The Fréchet distribution, also known as inverse Weibull distribution, [2] [3] is a special case of the generalized extreme value distribution. It has the cumulative distribution function ( ) = > . where α > 0 is a shape parameter.

  7. Fisher–Tippett–Gnedenko theorem - Wikipedia

    en.wikipedia.org/wiki/Fisher–Tippett–Gnedenko...

    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 (1936) [3] [5] [4] and was further developed by Gnedenko (1943).

  8. Maximum and minimum - Wikipedia

    en.wikipedia.org/wiki/Maximum_and_minimum

    However, the normalised sinc function (blue) has arg min of {−1.43, 1.43}, approximately, because their global minima occur at x = ±1.43, even though the minimum value is the same. [7] In mathematics , the arguments of the maxima (abbreviated arg max or argmax) and arguments of the minima (abbreviated arg min or argmin) are the input points ...

  9. Generalized Pareto distribution - Wikipedia

    en.wikipedia.org/wiki/Generalized_Pareto...

    It is of a particular interest in the extreme value theory to estimate the shape parameter , especially when is positive (so called the heavy-tailed distribution). Let F u {\displaystyle F_{u}} be their conditional excess distribution function.