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. -
Moments of the Product and Ratio of Two Correlated Chi-Square Variables
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Springer - Publisher Connector Stat Papers (2009) 50:581–592 DOI 10.1007/s00362-007-0105-0 REGULAR ARTICLE Moments of the product and ratio of two correlated chi-square variables Anwar H. Joarder Received: 2 June 2006 / Revised: 8 October 2007 / Published online: 20 November 2007 © The Author(s) 2007 Abstract The exact probability density function of a bivariate chi-square distribu- tion with two correlated components is derived. Some moments of the product and ratio of two correlated chi-square random variables have been derived. The ratio of the two correlated chi-square variables is used to compare variability. One such applica- tion is referred to. Another application is pinpointed in connection with the distribution of correlation coefficient based on a bivariate t distribution. Keywords Bivariate chi-square distribution · Moments · Product of correlated chi-square variables · Ratio of correlated chi-square variables Mathematics Subject Classification (2000) 62E15 · 60E05 · 60E10 1 Introduction Fisher (1915) derived the distribution of mean-centered sum of squares and sum of products in order to study the distribution of correlation coefficient from a bivariate nor- mal sample. Let X1, X2,...,X N (N > 2) be two-dimensional independent random vectors where X j = (X1 j , X2 j ) , j = 1, 2,...,N is distributed as a bivariate normal distribution denoted by N2(θ, ) with θ = (θ1,θ2) and a 2 × 2 covariance matrix = (σik), i = 1, 2; k = 1, 2. The sample mean-centered sums of squares and sum of products are given by a = N (X − X¯ )2 = mS2, m = N − 1,(i = 1, 2) ii j=1 ij i i = N ( − ¯ )( − ¯ ) = and a12 j=1 X1 j X1 X2 j X2 mRS1 S2, respectively. -
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 -
A Multivariate Student's T-Distribution
Open Journal of Statistics, 2016, 6, 443-450 Published Online June 2016 in SciRes. http://www.scirp.org/journal/ojs http://dx.doi.org/10.4236/ojs.2016.63040 A Multivariate Student’s t-Distribution Daniel T. Cassidy Department of Engineering Physics, McMaster University, Hamilton, ON, Canada Received 29 March 2016; accepted 14 June 2016; published 17 June 2016 Copyright © 2016 by author and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract A multivariate Student’s t-distribution is derived by analogy to the derivation of a multivariate normal (Gaussian) probability density function. This multivariate Student’s t-distribution can have different shape parameters νi for the marginal probability density functions of the multi- variate distribution. Expressions for the probability density function, for the variances, and for the covariances of the multivariate t-distribution with arbitrary shape parameters for the marginals are given. Keywords Multivariate Student’s t, Variance, Covariance, Arbitrary Shape Parameters 1. Introduction An expression for a multivariate Student’s t-distribution is presented. This expression, which is different in form than the form that is commonly used, allows the shape parameter ν for each marginal probability density function (pdf) of the multivariate pdf to be different. The form that is typically used is [1] −+ν Γ+((ν n) 2) T ( n) 2 +Σ−1 n 2 (1.[xx] [ ]) (1) ΓΣ(νν2)(π ) This “typical” form attempts to generalize the univariate Student’s t-distribution and is valid when the n marginal distributions have the same shape parameter ν . -
Extreme Value Theory and Backtest Overfitting in Finance
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Bowdoin College Bowdoin College Bowdoin Digital Commons Honors Projects Student Scholarship and Creative Work 2015 Extreme Value Theory and Backtest Overfitting in Finance Daniel C. Byrnes [email protected] Follow this and additional works at: https://digitalcommons.bowdoin.edu/honorsprojects Part of the Statistics and Probability Commons Recommended Citation Byrnes, Daniel C., "Extreme Value Theory and Backtest Overfitting in Finance" (2015). Honors Projects. 24. https://digitalcommons.bowdoin.edu/honorsprojects/24 This Open Access Thesis is brought to you for free and open access by the Student Scholarship and Creative Work at Bowdoin Digital Commons. It has been accepted for inclusion in Honors Projects by an authorized administrator of Bowdoin Digital Commons. For more information, please contact [email protected]. Extreme Value Theory and Backtest Overfitting in Finance An Honors Paper Presented for the Department of Mathematics By Daniel Byrnes Bowdoin College, 2015 ©2015 Daniel Byrnes Acknowledgements I would like to thank professor Thomas Pietraho for his help in the creation of this thesis. The revisions and suggestions made by several members of the math faculty were also greatly appreciated. I would also like to thank the entire department for their support throughout my time at Bowdoin. 1 Contents 1 Abstract 3 2 Introduction4 3 Background7 3.1 The Sharpe Ratio.................................7 3.2 Other Performance Measures.......................... 10 3.3 Example of an Overfit Strategy......................... 11 4 Modes of Convergence for Random Variables 13 4.1 Random Variables and Distributions...................... 13 4.2 Convergence in Distribution.......................... -
A Guide on Probability Distributions
powered project A guide on probability distributions R-forge distributions Core Team University Year 2008-2009 LATEXpowered Mac OS' TeXShop edited Contents Introduction 4 I Discrete distributions 6 1 Classic discrete distribution 7 2 Not so-common discrete distribution 27 II Continuous distributions 34 3 Finite support distribution 35 4 The Gaussian family 47 5 Exponential distribution and its extensions 56 6 Chi-squared's ditribution and related extensions 75 7 Student and related distributions 84 8 Pareto family 88 9 Logistic ditribution and related extensions 108 10 Extrem Value Theory distributions 111 3 4 CONTENTS III Multivariate and generalized distributions 116 11 Generalization of common distributions 117 12 Multivariate distributions 132 13 Misc 134 Conclusion 135 Bibliography 135 A Mathematical tools 138 Introduction This guide is intended to provide a quite exhaustive (at least as I can) view on probability distri- butions. It is constructed in chapters of distribution family with a section for each distribution. Each section focuses on the tryptic: definition - estimation - application. Ultimate bibles for probability distributions are Wimmer & Altmann (1999) which lists 750 univariate discrete distributions and Johnson et al. (1994) which details continuous distributions. In the appendix, we recall the basics of probability distributions as well as \common" mathe- matical functions, cf. section A.2. And for all distribution, we use the following notations • X a random variable following a given distribution, • x a realization of this random variable, • f the density function (if it exists), • F the (cumulative) distribution function, • P (X = k) the mass probability function in k, • M the moment generating function (if it exists), • G the probability generating function (if it exists), • φ the characteristic function (if it exists), Finally all graphics are done the open source statistical software R and its numerous packages available on the Comprehensive R Archive Network (CRAN∗). -
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)).