Extreme Value Theory and Applications
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Factor of Safety and Probability of Failure
Factor of safety and probability of failure Introduction How does one assess the acceptability of an engineering design? Relying on judgement alone can lead to one of the two extremes illustrated in Figure 1. The first case is economically unacceptable while the example illustrated in the drawing on the right violates all normal safety standards. Figure 1: Rockbolting alternatives involving individual judgement. (Drawings based on a cartoon in a brochure on rockfalls published by the Department of Mines of Western Australia.) Sensitivity studies The classical approach used in designing engineering structures is to consider the relationship between the capacity C (strength or resisting force) of the element and the demand D (stress or disturbing force). The Factor of Safety of the structure is defined as F = C/D and failure is assumed to occur when F is less than unity. Factor of safety and probability of failure Rather than base an engineering design decision on a single calculated factor of safety, an approach which is frequently used to give a more rational assessment of the risks associated with a particular design is to carry out a sensitivity study. This involves a series of calculations in which each significant parameter is varied systematically over its maximum credible range in order to determine its influence upon the factor of safety. This approach was used in the analysis of the Sau Mau Ping slope in Hong Kong, described in detail in another chapter of these notes. It provided a useful means of exploring a range of possibilities and reaching practical decisions on some difficult problems. -
An Overview of the Extremal Index Nicholas Moloney, Davide Faranda, Yuzuru Sato
An overview of the extremal index Nicholas Moloney, Davide Faranda, Yuzuru Sato To cite this version: Nicholas Moloney, Davide Faranda, Yuzuru Sato. An overview of the extremal index. Chaos: An Interdisciplinary Journal of Nonlinear Science, American Institute of Physics, 2019, 29 (2), pp.022101. 10.1063/1.5079656. hal-02334271 HAL Id: hal-02334271 https://hal.archives-ouvertes.fr/hal-02334271 Submitted on 28 Oct 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. An overview of the extremal index Nicholas R. Moloney,1, a) Davide Faranda,2, b) and Yuzuru Sato3, c) 1)Department of Mathematics and Statistics, University of Reading, Reading RG6 6AX, UKd) 2)Laboratoire de Sciences du Climat et de l’Environnement, UMR 8212 CEA-CNRS-UVSQ,IPSL, Universite Paris-Saclay, 91191 Gif-sur-Yvette, France 3)RIES/Department of Mathematics, Hokkaido University, Kita 20 Nishi 10, Kita-ku, Sapporo 001-0020, Japan (Dated: 5 January 2019) For a wide class of stationary time series, extreme value theory provides limiting distributions for rare events. The theory describes not only the size of extremes, but also how often they occur. In practice, it is often observed that extremes cluster in time. -
Probabilistic Stability Analysis
TABLE OF CONTENTS Page A-7 Probabilistic Limit State Analysis ....................................................... A-7-1 A-7.1 Key Concepts ....................................................................... A-7-1 A-7.2 Example: FOSM and MC Analysis for Heave at the Toe of a Levee ................................................................. A-7-4 A-7.3 Example: RCC Gravity Dam Stability .............................. A-7-14 A-7.4 Example: Screening-Level Check of Embankment Post-Liquefaction Stability ............................................ A-7-19 A-7.5 Example: Foundation Rock Wedge Stability .................... A-7-23 A-7.6 Model Uncertainty ............................................................. A-7-26 A-7.7 References .......................................................................... A-7-28 Tables Page A-7-1 Variables in Levee Heave Analysis ................................................. A-7-7 A-7-2 FOSM Calculations for Water Surface at Levee Crest .................... A-7-8 A-7-3 Variable Distributions for MC Simulation .................................... A-7-11 A-7-4 Comparison of MC and FOSM Analysis Results .......................... A-7-12 A-7-5 Correlation Coefficients for Water Surface at the Levee Crest ..... A-7-13 A-7-6 Comparison of MC and FOSM Sensitivity Analysis Results ........ A-7-13 A-7-7 Summary of Concrete Input Properties .......................................... A-7-16 A-7-8 RCC Dam Sensitivity Rankings..................................................... A-7-18 A-7-9 Summary -
Using Extreme Value Theory to Test for Outliers Résumé Abstract
Using Extreme Value Theory to test for Outliers Nathaniel GBENRO(*)(**) (*)Ecole Nationale Supérieure de Statistique et d’Economie Appliquée (**) Université de Cergy Pontoise [email protected] Mots-clés. Extreme Value Theory, Outliers, Hypothesis Tests, etc Résumé Dans cet article, il est proposé une procédure d’identification des outliers basée sur la théorie des valeurs extrêmes (EVT). Basé sur un papier de Olmo(2009), cet article généralise d’une part l’algorithme proposé puis d’autre part teste empiriquement le comportement de la procédure sur des données simulées. Deux applications du test sont conduites. La première application compare la nouvelle procédure de test et le test de Grubbs sur des données générées à partir d’une distribution normale. La seconde application utilisent divers autres distributions (autre que normales) pour analyser le comportement de l’erreur de première espèce en absence d’outliers. Les résultats de ces deux applications suggèrent que la nouvelle procédure de test a une meilleure performance que celle de Grubbs. En effet, la première application conclut au fait lorsque les données sont générées d’une distribution normale, les deux tests ont une erreur de première espèces quasi identique tandis que la nouvelle procédure a une puissance plus élevée. La seconde application, quant à elle, indique qu’en absence d’outlier dans un échantillon de données issue d’une distribution autre que normale, le test de Grubbs identifie quasi-systématiquement la valeur maximale comme un outlier ; La nouvelle procédure de test permettant de corriger cet fait. Abstract This paper analyses the identification of aberrant values using a new approach based on the extreme value theory (EVT). -
Extreme Value Theory Page 1
SP9-20: Extreme value theory Page 1 Extreme value theory Syllabus objectives 4.5 Explain the importance of the tails of distributions, tail correlations and low frequency / high severity events. 4.6 Demonstrate how extreme value theory can be used to help model risks that have a low probability. The Actuarial Education Company © IFE: 2019 Examinations Page 2 SP9-20: Extreme value theory 0 Introduction In this module we look more closely at the tails of distributions. In Section 2, we introduce the concept of extreme value theory and discuss its importance in helping to manage risk. The importance of tail distributions and correlations has already been mentioned in Module 15. In this module, the idea is extended to consider the modelling of risks with low frequency but high severity, including the use of extreme value theory. Note the following advice in the Core Reading: Beyond the Core Reading, students should be able to recommend a specific approach and choice of model for the tails based on a mixture of quantitative analysis and graphical diagnostics. They should also be able to describe how the main theoretical results can be used in practice. © IFE: 2019 Examinations The Actuarial Education Company SP9-20: Extreme value theory Page 3 Module 20 – Task list Task Completed Read Section 1 of this module and answer the self-assessment 1 questions. Read: 2 Sweeting, Chapter 12, pages 286 – 293 Read the remaining sections of this module and answer the self- 3 assessment questions. This includes relevant Core Reading for this module. 4 Attempt the practice questions at the end of this module. -
Going Beyond the Hill: an Introduction to Multivariate Extreme Value Theory
Motivation Basics MRV Max-stable MEV PAM MOM BHM Spectral Going beyond the Hill: An introduction to Multivariate Extreme Value Theory Philippe Naveau [email protected] Laboratoire des Sciences du Climat et l’Environnement (LSCE) Gif-sur-Yvette, France Books: Coles (1987), Embrechts et al. (1997), Resnick (2006) FP7-ACQWA, GIS-PEPER, MIRACLE & ANR-McSim, MOPERA 22 juillet 2014 Motivation Basics MRV Max-stable MEV PAM MOM BHM Spectral Statistics and Earth sciences “There is, today, always a risk that specialists in two subjects, using languages QUIZ full of words that are unintelligible without study, (A) Gilbert Walker will grow up not only, without (B) Ed Lorenz knowledge of each other’s (C) Rol Madden work, but also will ignore the (D) Francis Zwiers problems which require mutual assistance”. Motivation Basics MRV Max-stable MEV PAM MOM BHM Spectral EVT = Going beyond the data range What is the probability of observingHauteurs data de abovecrête (Lille, an high1895-2002) threshold ? 100 80 60 Precipitationen mars 40 20 0 1900 1920 1940 1960 1980 2000 Années March precipitation amounts recorded at Lille (France) from 1895 to 2002. The 17 black dots corresponds to the number of exceedances above the threshold un = 75 mm. This number can be conceptually viewed as a random sum of Bernoulli (binary) events. Motivation Basics MRV Max-stable MEV PAM MOM BHM Spectral An example in three dimensions Air pollutants (Leeds, UK, winter 94-98, daily max) NO vs. PM10 (left), SO2 vs. PM10 (center), and SO2 vs. NO (right) (Heffernan& Tawn 2004, Boldi -
Hyperbolicity and Stable Polynomials in Combinatorics and Probability
Hyperbolicity and stable polynomials in combinatorics and probability Robin Pemantle 1,2 ABSTRACT: These lectures survey the theory of hyperbolic and stable polynomials, from their origins in the theory of linear PDE’s to their present uses in combinatorics and probability theory. Keywords: amoeba, cone, dual cone, G˚arding-hyperbolicity, generating function, half-plane prop- erty, homogeneous polynomial, Laguerre–P´olya class, multiplier sequence, multi-affine, multivariate stability, negative dependence, negative association, Newton’s inequalities, Rayleigh property, real roots, semi-continuity, stochastic covering, stochastic domination, total positivity. Subject classification: Primary: 26C10, 62H20; secondary: 30C15, 05A15. 1Supported in part by National Science Foundation grant # DMS 0905937 2University of Pennsylvania, Department of Mathematics, 209 S. 33rd Street, Philadelphia, PA 19104 USA, pe- [email protected] Contents 1 Introduction 1 2 Origins, definitions and properties 4 2.1 Relation to the propagation of wave-like equations . 4 2.2 Homogeneous hyperbolic polynomials . 7 2.3 Cones of hyperbolicity for homogeneous polynomials . 10 3 Semi-continuity and Morse deformations 14 3.1 Localization . 14 3.2 Amoeba boundaries . 15 3.3 Morse deformations . 17 3.4 Asymptotics of Taylor coefficients . 19 4 Stability theory in one variable 23 4.1 Stability over general regions . 23 4.2 Real roots and Newton’s inequalities . 26 4.3 The Laguerre–P´olya class . 31 5 Multivariate stability 34 5.1 Equivalences . 36 5.2 Operations preserving stability . 38 5.3 More closure properties . 41 6 Negative dependence 41 6.1 A brief history of negative dependence . 41 6.2 Search for a theory . 43 i 6.3 The grail is found: application of stability theory to joint laws of binary random variables . -
Geometric Stable Laws Through Series Representations
Serdica Math. J. 25 (1999), 241-256 GEOMETRIC STABLE LAWS THROUGH SERIES REPRESENTATIONS Tomasz J. Kozubowski, Krzysztof Podg´orski Communicated by S. T. Rachev Abstract. Let (Xi) be a sequence of i.i.d. random variables, and let N be a geometric random variable independent of (Xi). Geometric stable distributions are weak limits of (normalized) geometric compounds, SN = X + + XN , when the mean of N converges to infinity. By an appro- 1 · · · priate representation of the individual summands in SN we obtain series representation of the limiting geometric stable distribution. In addition, we [Nt] study the asymptotic behavior of the partial sum process SN (t) = Xi, i=1 and derive series representations of the limiting geometric stable processP and the corresponding stochastic integral. We also obtain strong invariance principles for stable and geometric stable laws. 1. Introduction. An increasing interest has been seen recently in geo- metric stable (GS) distributions: the class of limiting laws of appropriately nor- malized random sums of i.i.d. random variables, (1) S = X + + X , N 1 · · · N 1991 Mathematics Subject Classification: 60E07, 60F05, 60F15, 60F17, 60G50, 60H05 Key words: geometric compound, invariance principle, Linnik distribution, Mittag-Leffler distribution, random sum, stable distribution, stochastic integral 242 Tomasz J. Kozubowski, Krzysztof Podg´orski where the number of terms is geometrically distributed with mean 1/p, and p 0 → (see, e.g., [7], [8], [10], [11], [12], [13], [14] and [21]). These heavy-tailed distribu- tions provide useful models in mathematical finance (see, e.g., [1], [20], [13]), as well as in a variety of other fields (see, e.g., [6] for examples, applications, and extensive references for geometric compounds (1)). -
View Article
Fire Research Note No. 837 SOME POSSIBLE APPLICATIONS OF THE THEORY OF EXTREME VALUES FOR THE ANALYSIS OF FIRE LOSS DATA . .' by G. RAMACHANDRAN September 1970 '. FIRE "RESEARCH STATION © BRE Trust (UK) Permission is granted for personal noncommercial research use. Citation of the work is allowed and encouraged. " '.. .~ t . i\~ . f .." ~ , I "I- I Fire Research Station, Borehamwood, He-rts. Tel. 81-953-6177 F.R.. Note No.837. t".~~. ~ . ~~:_\": -~: SOME POSSIBLE APPLIC AT 10m OF THE THEORY OF EXTREME VALUES ,'c'\ .r: FOR THE ANALYSIS OF FIRE LOSS DATA by G. Ramachandran SUMMARY This paper discusses the possibili~ of applying the statistical theory of extreme values to data on monetary losses due to large fires in buildings • ." The theory is surveyed in order to impart the necessary background picture. With the logarithm of loss as the variate, an initial distribution of the exponential type is assumed. Hence use of the first asymptotic distribution of largest values is illustrated. Extreme order statistics other than the largest are also discussed. Uses of these statistics are briefly outlined. Suggestions for further research are also made. I KEY WORDS: Large fires, loss, fire statistics. i ' ~ Crown copyright .,. ~.~ This repor-t has not been published and should be considerec as confidential odvonce information. No rClferClnce should be made to it in any publication without the written consent of the DirClctor of Fire Research. MINISTRY OF TECHNOLOGY AND FIRE OFFICES' COMMITTEE JOINT FIRE RESEARCH ORGANIZATION F.R. Note ·No.837. SOME POSSIBLE APPLICATIONS OF TliE THEORY OF EXTREME VALUES FOR THE ANALYSIS OF FIRE LOSS DATA by G. -
An Introduction to Extreme Value Statistics
An Introduction to Extreme Value Statistics Marielle Pinheiro and Richard Grotjahn ii This tutorial is a basic introduction to extreme value analysis and the R package, extRemes. Extreme value analysis has application in a number of different disciplines ranging from finance to hydrology, but here the examples will be presented in the form of climate observations. We will begin with a brief background on extreme value analysis, presenting the two main methods and then proceeding to show examples of each method. Readers interested in a more detailed explanation of the math should refer to texts such as Coles 2001 [1], which is cited frequently in the Gilleland and Katz extRemes 2.0 paper [2] detailing the various tools provided in the extRemes package. Also, the extRemes documentation, which is useful for functions syntax, can be found at http://cran.r-project.org/web/packages/extRemes/ extRemes.pdf For guidance on the R syntax and R scripting, many resources are available online. New users might want to begin with the Cookbook for R (http://www.cookbook-r.com/ or Quick-R (http://www.statmethods.net/) Contents 1 Background 1 1.1 Extreme Value Theory . .1 1.2 Generalized Extreme Value (GEV) versus Generalized Pareto (GP) . .2 1.3 Stationarity versus non-stationarity . .3 2 ExtRemes example: Using Davis station data from 1951-2012 7 2.1 Explanation of the fevd input and output . .7 2.1.1 fevd tools used in this example . .8 2.2 Working with the data: Generalized Extreme Value (GEV) distribution fit . .9 2.3 Working with the data: Generalized Pareto (GP) distribution fit . -
Extreme Value Theory (Or How to Go Beyond of the Range Data)
Extreme Value Theory (or how to go beyond of the range data) Sept 2007, Romania Philippe Naveau Laboratoire des Sciences du Climat et l’Environnement (LSCE) Gif-sur-Yvette, France Katz et al., Statistics of extremes in hydrology, Advances in Water Resources 25 (2002) 1287-1304 Motivation Univariate EVT Non-stationary extremes Spatial extremes Conclusions Extreme quotes 1 “Man can believe the impossible, but man can never believe the improbable” Oscar Wilde (Intentions, 1891) 2 “Il est impossible que l’improbable n’arrive jamais” Emil Julius Gumbel (1891-1966) Extreme events ? ... a probabilistic concept linked to the tail behavior : low frequency of occurrence, large uncertainty and sometimes strong amplitude. Region of interest Motivation Univariate EVT Non-stationary extremes Spatial extremes Conclusions Important issues in Extreme Value Theory Applied statistics An asymptotic probabilistic concept Non-stationarity A statistical modeling approach Identifying clearly assumptions Univariate Multivariate Non-parametric Parametric Assessing uncertainties Goodness of fit and model selection Independence Theoritical probability Motivation Univariate EVT Non-stationary extremes Spatial extremes Conclusions Outline 1 Motivation Heavy rainfalls Three applications 2 Univariate EVT Asymptotic result Historical perspective GPD Parameters estimation Brief summary of univariate iid EVT 3 Non-stationary extremes Spatial interpolation of return levels Downscaling of heavy rainfalls 4 Spatial extremes Assessing spatial dependences among maxima 5 Conclusions -
Zbigniew J. Jurek ¿-STABILITY and RANDOM INTEGRAL
DEMONSTRATIO MATHEMATICA Vol. XXIX No 2 1996 Zbigniew J. Jurek ¿-STABILITY AND RANDOM INTEGRAL REPRESENTATIONS OF LIMIT LAWS Introduction In the first part, the notion of ¿-stability is presented using a semigroup of non-linear shrinking transformations. This may have some potential appli- cations in technical sciences. Then, two open problems associated with these transformations are discussed. In the second part, the advantages of random integral representations are illustrated on classes Up, which are limit distri- butions of some averages of independent Levy processes. It is shown that the random integral mappings induced by these representations are homeomor- phisms between appropriate convolution semigroups. Stable measures are characterized as invariant elements of these random integral mappings. Us- ing the classes Up a sub classification of the class ID of all infinitely divisible measures is given. 1. ¿-stable distributions Contemplating the notion of stability, in the probability theory, one can notice that it is featured by three properties. First, one deals with samples of observations (i.e., independent identically distributed random variables). Second, the observed variables are modified (normalized) by some trans- formations (mappings on the space where the random variables take on their values). Third, on the modified sample of observations the operation in underlying space is performed and then the limit distributions of such constructed sequences are investigated. To be more specific let us illustrate the above steps by well-known examples. EXAMPLE 1 (stable distributions). The underlying space is R, Tax := ax, a > 0, x 6 R, are the transformations on R and "+" is the operation in R. So, (XUX2, • • -iXk) is an observed sample, (TakX\,TakX2,.