Risk Measurement Using Extreme Values Theory

2017 ◽  
Author(s):  
Sebastian Maio ◽  
pablo macri ◽  
Manuel Maurette
Keyword(s):  
Extreme Values ◽  
Risk Measurement ◽  
Extreme Values Theory

scholarly journals Maximum Values of Frozen Soil’s Depth and Hoar Frost’s Thickness Estimated on the Basis of Extreme Values Theory

2018 ◽  
Vol 12 (2) ◽  
pp. 13-23
Author(s):  
Maria Nedealcov ◽  
Valentin Răileanu ◽  
Gheorghe Croitoru ◽  
Cojocari Rodica ◽  
Crivova Olga
Keyword(s):  
Risk Factors ◽  
Extreme Value Theory ◽  
Extreme Values ◽  
Gumbel Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Frozen Soil ◽  
Return Interval ◽  
The Mean ◽  
Extreme Values Theory

Abstract Extreme climatic phenomena present risk factors for agriculture, health, constructions, etc. and are studied profoundly these past years using extreme value theory. Several relation that describe positive extreme values’ probability Generalized Extreme Value and Gumbel distribution are presented in the article. As a example, we show the maps of characteristic and reference values of the maximum depth of the frozen soil and thickness of hoar-frost with a probability of exceeding per year equal to 0,02, which is equivalent to the mean return interval of 50 years. The obtained results could serve as a base for elaboration of national annexes in constructions.

scholarly journals Data Geometry and Extreme Value Distribution

2021 ◽  
Vol 2 (2) ◽  
pp. 06-15
Author(s):  
Mamadou Cisse ◽  
Aliou Diop ◽  
Souleymane Bognini ◽  
Nonvikan Karl-Augustt ALAHASSA
Keyword(s):  
Weibull Distribution ◽  
Extreme Values ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Data System ◽  
Peaks Over Threshold ◽  
Stochastic Graphs ◽  
Block Maxima ◽  
Extreme Values Theory

In extreme values theory, there exist two approaches about data treatment: block maxima and peaks-over-threshold (POT) methods, which take in account data over a fixed value. But, those approaches are limited. We show that if a certain geometry is modeled with stochastic graphs, probabilities computed with Generalized Extreme Value (GEV) Distribution can be deflated. In other words, taking data geometry in account change extremes distribution. Otherwise, it appears that if the density characterizing the states space of data system is uniform, and if the quantile studied is positive, then the Weibull distribution is insensitive to data geometry, when it is an area attraction, and the Fréchet distribution becomes the less inflationary.

Extreme Values Theory and Return Level Analysisfor Catastrophe Prediction

2014 ◽  
Vol 23 (2) ◽  
pp. 124-135
Author(s):  
Amitesh Kapoor ◽  
Utkarsh Shrivastava
Keyword(s):  
Extreme Values ◽  
Return Level ◽  
Extreme Values Theory

scholarly journals Computing absorbing times via fluid approximations

2017 ◽  
Vol 49 (3) ◽  
pp. 768-790 ◽  
Author(s):  
Nicolas Gast ◽  
Bruno Gaujal
Keyword(s):  
Extreme Values ◽  
Mutual Exclusion ◽  
High Dimensional ◽  
Fluid Approximation ◽  
Discrete Time Markov Chain ◽  
Absorbing State ◽  
Fluid Approximations ◽  
Coupon Collector ◽  
Extreme Values Theory ◽  
First Time

AbstractIn this paper we compute the absorbing timeTnof ann-dimensional discrete-time Markov chain comprisingncomponents, each with an absorbing state and evolving in mutual exclusion. We show that the random absorbing timeTnis well approximated by a deterministic timetnthat is the first time when a fluid approximation of the chain approaches the absorbing state at a distance 1 /n. We provide an asymptotic expansion oftnthat uses the spectral decomposition of the kernel of the chain as well as the asymptotic distribution ofTn, relying on extreme values theory. We show the applicability of this approach with three different problems: the coupon collector, the erasure channel lifetime, and the coupling times of random walks in high-dimensional spaces.

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