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Titlebook: Extreme Values In Random Sequences; Pavle Mladenović Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive license to

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https://doi.org/10.1007/978-81-322-3753-2t into the natural connections between probability theory and algebra, combinatorics, graph theory and combinatorial geometry. Extreme value distributions, generalized Pareto distributions and many other distributions as well appear in theorems that describe the asymptotic behavior of the extremal c
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Time Series and Missing Observations,ribution of a random vector whose components are the maximum in a complete sample and the maximum of observed random variables is uniquely determined by the asymptotic relative frequency of observed random variables. This is not the case unless the conditions of weak dependence are satisfied. The as
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Combinatorial Problems and Extreme Values,t into the natural connections between probability theory and algebra, combinatorics, graph theory and combinatorial geometry. Extreme value distributions, generalized Pareto distributions and many other distributions as well appear in theorems that describe the asymptotic behavior of the extremal c
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978-3-031-57414-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Extreme Values In Random Sequences978-3-031-57412-2Series ISSN 1431-8598 Series E-ISSN 2197-1773
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Christopher K. Frantz,Saba Siddiki33. Regularly varying functions appear to be a very useful tool in probability theory, especially in the theory of the summation of random variables, probabilistic extreme value theory, modeling of random heavy tail phenomena, and other important topics. In 1943, Gnedenko published his seminal paper
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Institutions and International Business,pet obtained three possible forms of the limiting distributions of maxima, called extreme value distributions, in the sequences of independent and identically distributed random variables. Von Mises obtained the sufficient conditions for an absolutely continuous distribution function to belong to an
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