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On approximating dependence function and its derivatives Extremes (IF 1.3) Pub Date : 2024-03-15 Nader Tajvidi
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Extremes for stationary regularly varying random fields over arbitrary index sets Extremes (IF 1.3) Pub Date : 2024-01-25 Riccardo Passeggeri, Olivier Wintenberger
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Extremes of locally-homogenous vector-valued Gaussian processes Extremes (IF 1.3) Pub Date : 2024-01-08 Pavel Ievlev
In this paper, we study the asymptotical behaviour of high exceedence probabilities for centered continuous \(\mathbb {R}^n\)-valued Gaussian random field \(\varvec{X}\) with covariance matrix satisfying \(\Sigma - R ( t + s, t ) \sim \sum _{l = 1}^n B_l ( t ) \, | s_l |^{\alpha _l}\) as \(s \downarrow 0\). Such processes occur naturally as time transformations of homogenous random fields, and we present
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Point process convergence for symmetric functions of high-dimensional random vectors Extremes (IF 1.3) Pub Date : 2023-12-20 Johannes Heiny, Carolin Kleemann
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Regional pooling in extreme event attribution studies: an approach based on multiple statistical testing Extremes (IF 1.3) Pub Date : 2023-12-16 Leandra Zanger, Axel Bücher, Frank Kreienkamp, Philip Lorenz, Jordis S. Tradowsky
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Tail adversarial stability for regularly varying linear processes and their extensions Extremes (IF 1.3) Pub Date : 2023-12-13 Shuyang Bai, Ting Zhang
The notion of tail adversarial stability has been proven useful in obtaining limit theorems for tail dependent time series. Its implication and advantage over the classical strong mixing framework has been examined for max-linear processes, but not yet studied for additive linear processes. In this article, we fill this gap by verifying the tail adversarial stability condition for regularly varying
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Causality in extremes of time series Extremes (IF 1.3) Pub Date : 2023-10-31 Juraj Bodik, Milan Paluš, Zbyněk Pawlas
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Random networks with heterogeneous reciprocity Extremes (IF 1.3) Pub Date : 2023-09-25 Tiandong Wang, Sidney Resnick
Users of social networks display diversified behavior and online habits. For instance, a user’s tendency to reply to a post can depend on the user and the person posting. For convenience, we group users into aggregated behavioral patterns, focusing here on the tendency to reply to or reciprocate messages. The reciprocity feature in social networks reflects the information exchange among users. We study
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Weighted weak convergence of the sequential tail empirical process for heteroscedastic time series with an application to extreme value index estimation Extremes (IF 1.3) Pub Date : 2023-08-24 Tobias Jennessen, Axel Bücher
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A modeler’s guide to extreme value software Extremes (IF 1.3) Pub Date : 2023-08-03 Léo R. Belzile, Christophe Dutang, Paul J. Northrop, Thomas Opitz
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Gradient boosting for extreme quantile regression Extremes (IF 1.3) Pub Date : 2023-07-21 Jasper Velthoen, Clément Dombry, Juan-Juan Cai, Sebastian Engelke
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High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields Extremes (IF 1.3) Pub Date : 2023-07-12 Emma S. Simpson, Thomas Opitz, Jennifer L. Wadsworth
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Tail processes and tail measures: An approach via Palm calculus Extremes (IF 1.3) Pub Date : 2023-06-27 Günter Last
Using an intrinsic approach, we study some properties of random fields which appear as tail fields of regularly varying stationary random fields. The index set is allowed to be a general locally compact Hausdorff Abelian group \({\mathbb G}\). The values are taken in a measurable cone, equipped with a pseudo norm. We first discuss some Palm formulas for the exceedance random measure \(\xi\) associated
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Tail-dependence, exceedance sets, and metric embeddings Extremes (IF 1.3) Pub Date : 2023-05-27 Anja Janßen, Sebastian Neblung, Stilian Stoev
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Large nearest neighbour balls in hyperbolic stochastic geometry Extremes (IF 1.3) Pub Date : 2023-04-20 Moritz Otto, Christoph Thäle
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Remembering Ross Leadbetter: some personal recollections Extremes (IF 1.3) Pub Date : 2023-04-10 Tailen Hsing, Holger Rootzén
Ross Leadbetter had had a broad and deep influence on the development of probabilistic and statistical theory of extreme values and on the application of extreme-value methods. He has been an inspiration and a friend for many of us. This editorial collects thirteen personal recollections of Ross and his work. An account of his career and some of his work can be found in the IMS Obituary “Ross Leadbetter
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Extremes of Markov random fields on block graphs: Max-stable limits and structured Hüsler–Reiss distributions Extremes (IF 1.3) Pub Date : 2023-04-04 Stefka Asenova, Johan Segers
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A marginal modelling approach for predicting wildfire extremes across the contiguous United States Extremes (IF 1.3) Pub Date : 2023-04-01 Eleanor D’Arcy, Callum J. R. Murphy-Barltrop, Rob Shooter, Emma S. Simpson
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A weighted composite log-likelihood approach to parametric estimation of the extreme quantiles of a distribution Extremes (IF 1.3) Pub Date : 2023-03-29 Michael L. Stein
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Editorial: EVA 2021 data challenge on spatiotemporal prediction of wildfire extremes in the USA Extremes (IF 1.3) Pub Date : 2023-03-27 Thomas Opitz
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Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach Extremes (IF 1.3) Pub Date : 2023-03-14 Zhongwei Zhang, Elias Krainski, Peng Zhong, Harvard Rue, Raphaël Huser
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A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes Extremes (IF 1.3) Pub Date : 2023-02-21 Daniela Cisneros, Yan Gong, Rishikesh Yadav, Arnab Hazra, Raphaël Huser
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Analysis of wildfires and their extremes via spatial quantile autoregressive model Extremes (IF 1.3) Pub Date : 2023-02-13 Jongmin Lee, Joonpyo Kim, Joonho Shin, Seongjin Cho, Seongmin Kim, Kyoungjae Lee
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Gradient boosting with extreme-value theory for wildfire prediction Extremes (IF 1.3) Pub Date : 2023-01-21 Jonathan Koh
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Reconstruction of incomplete wildfire data using deep generative models Extremes (IF 1.3) Pub Date : 2023-01-18 Tomislav Ivek, Domagoj Vlah
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Multi-normex distributions for the sum of random vectors. Rates of convergence Extremes (IF 1.3) Pub Date : 2023-01-13 Marie Kratz, Evgeny Prokopenko
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A refined Weissman estimator for extreme quantiles Extremes (IF 1.3) Pub Date : 2022-12-27 Michaël Allouche, Jonathan El Methni, Stéphane Girard
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Simple random forest classification algorithms for predicting occurrences and sizes of wildfires Extremes (IF 1.3) Pub Date : 2022-12-27 David Makowski
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Causal modelling of heavy-tailed variables and confounders with application to river flow Extremes (IF 1.3) Pub Date : 2022-12-17 Olivier C. Pasche, Valérie Chavez-Demoulin, Anthony C. Davison
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Exchangeable min-id sequences: Characterization, exponent measures and non-decreasing id-processes Extremes (IF 1.3) Pub Date : 2022-12-17 Florian Brück, Jan-Frederik Mai, Matthias Scherer
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Running minimum in the best-choice problem Extremes (IF 1.3) Pub Date : 2022-11-29 Alexander Gnedin, Patryk Kozieł, Małgorzata Sulkowska
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Conditions for finiteness and bounds on moments of record values from iid continuous life distributions Extremes (IF 1.3) Pub Date : 2022-11-09 Tomasz Rychlik, Magdalena Szymkowiak
We consider the standard and kth record values arising in sequences of independent identically distributed continuous and positive random variables with finite expectations. We determine necessary and sufficient conditions on the type of record k, its number n and moment order r so that the rth moment of the n value of kth record is finite for every parent distribution. Under the conditions we present
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Extremal characteristics of conditional models Extremes (IF 1.3) Pub Date : 2022-11-10 Stan Tendijck, Jonathan Tawn, Philip Jonathan
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Asymptotic behavior of an intrinsic rank-based estimator of the Pickands dependence function constructed from B-splines Extremes (IF 1.3) Pub Date : 2022-11-09 Axel Bücher, Christian Genest, Richard A. Lockhart, Johanna G. Nešlehová
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Palm theory for extremes of stationary regularly varying time series and random fields Extremes (IF 1.3) Pub Date : 2022-10-24 Hrvoje Planinić
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Regression-type analysis for multivariate extreme values Extremes (IF 1.3) Pub Date : 2022-10-21 Miguel de Carvalho, Alina Kumukova, Gonçalo dos Reis
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Integral Functionals and the Bootstrap for the Tail Empirical Process Extremes (IF 1.3) Pub Date : 2022-10-14 B. Gail Ivanoff , Rafal Kulik, Hicham Loukrati
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Limit theorems for branching processes with immigration in a random environment Extremes (IF 1.3) Pub Date : 2022-07-12 Bojan Basrak, Péter Kevei
We investigate branching processes with immigration in a random environment. Using Goldie’s implicit renewal theory we prove that under a generalized Cramér condition the stationary distribution of such processes has a power law tail. We further show how several methods familiar in the extreme value theory provide a natural and elegant path to their mathematical analysis. In particular, we rely on
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The asymptotic distribution of the condition number for random circulant matrices Extremes (IF 1.3) Pub Date : 2022-07-04 Gerardo Barrera, Paulo Manrique-Mirón
In this manuscript, we study the limiting distribution for the joint law of the largest and the smallest singular values for random circulant matrices with generating sequence given by independent and identically distributed random elements satisfying the so-called Lyapunov condition. Under an appropriated normalization, the joint law of the extremal singular values converges in distribution, as the
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Improved interexceedance-times-based estimator of the extremal index using truncated distribution Extremes (IF 1.3) Pub Date : 2022-06-24 Jan Holešovský, Michal Fusek
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Functional strong law of large numbers for Betti numbers in the tail Extremes (IF 1.3) Pub Date : 2022-05-31 Takashi Owada, Zifu Wei
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Tail probabilities of random linear functions of regularly varying random vectors Extremes (IF 1.3) Pub Date : 2022-05-26 Bikramjit Das, Vicky Fasen-Hartmann, Claudia Klüppelberg
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Continuous simulation of storm processes Extremes (IF 1.3) Pub Date : 2022-05-09 Marine Demangeot, Rémi Carnec, Emilie Chautru, Christian Lantuéjoul
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Adapting the Hill estimator to distributed inference: dealing with the bias Extremes (IF 1.3) Pub Date : 2022-05-07 Liujun Chen, Deyuan Li, Chen Zhou
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Asymptotic dependence of in- and out-degrees in a preferential attachment model with reciprocity Extremes (IF 1.3) Pub Date : 2022-04-30 Tiandong Wang, Sidney I. Resnick
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Environmental contours as Voronoi cells Extremes (IF 1.3) Pub Date : 2022-04-29 Andreas Hafver, Christian Agrell, Erik Vanem
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On the asymptotic distribution of the scan statistic for empirical distributions Extremes (IF 1.3) Pub Date : 2022-02-21 Andrew Ying, Wen-Xin Zhou
This paper investigates the asymptotic behavior of several variants of the scan statistic for empirical distributions, which can be applied to detect the presence of an anomalous interval of any given length. In particular, we are interested in a Studentized scan statistic that is often preferable in practice. The main ingredients of our proof include Kolmogorov’s theorem, Poisson approximation, and
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Heavy-tailed phase-type distributions: a unified approach Extremes (IF 1.3) Pub Date : 2022-02-16 Martin Bladt, Jorge Yslas
A phase-type distribution is the distribution of the time until absorption in a finite state-space time-homogeneous Markov jump process, with one absorbing state and the rest being transient. These distributions are mathematically tractable and conceptually attractive to model physical phenomena due to their interpretation in terms of a hidden Markov structure. Three recent extensions of regular phase-type
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Modeling spatial extremes using normal mean-variance mixtures Extremes (IF 1.3) Pub Date : 2022-01-31 Zhongwei Zhang, Raphaël Huser, Thomas Opitz, Jennifer Wadsworth
Classical models for multivariate or spatial extremes are mainly based upon the asymptotically justified max-stable or generalized Pareto processes. These models are suitable when asymptotic dependence is present, i.e., the joint tail decays at the same rate as the marginal tail. However, recent environmental data applications suggest that asymptotic independence is equally important and, unfortunately
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Handling missing extremes in tail estimation Extremes (IF 1.3) Pub Date : 2021-12-23 Hui Xu, Richard Davis, Gennady Samorodnitsky
In some data sets, it may be the case that a portion of the extreme observations is missing. This might arise in cases where the extreme observations are just not available or are imprecisely measured. For example, considering human lifetimes, a topic of recent interest, birth certificates of centenarians may not even exist and many such individuals may not even be included in the data sets that are
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Extremal linkage networks Extremes (IF 1.3) Pub Date : 2021-12-03 Markus Heydenreich, Christian Hirsch
We demonstrate how sophisticated graph properties, such as small distances and scale-free degree distributions, arise naturally from a reinforcement mechanism on layered graphs. Every node is assigned an a-priori i.i.d. fitness with max-stable distribution. The fitness determines the node attractiveness w.r.t. incoming edges as well as the spatial range for outgoing edges. For max-stable fitness distributions
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Testing mean changes by maximal ratio statistics Extremes (IF 1.3) Pub Date : 2021-11-17 Gudan, Jovita, Račkauskas, Alfredas, Suquet, Charles
We propose a new test statistic \(\mathrm {MR}_{\gamma ,n}\) for detecting a changed segment in the mean, at unknown dates, in a regularly varying sample. Our model supports several alternatives of shifts in the mean, including one change point, constant, epidemic and linear form of a change. Our aim is to detect a short length changed segment \(\ell ^{*}\), assuming \(\ell^*/n\) to be small as the
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Extremal lifetimes of persistent cycles Extremes (IF 1.3) Pub Date : 2021-10-30 Chenavier, Nicolas, Hirsch, Christian
Persistent homology captures the appearances and disappearances of topological features such as loops and cavities when growing disks centered at a Poisson point process. We study extreme values for the lifetimes of features dying in bounded components and with birth resp. death time bounded away from the threshold for continuum percolation and the coexistence region. First, we describe the scaling
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Extremes of censored and uncensored lifetimes in survival data Extremes (IF 1.3) Pub Date : 2021-10-09 Maller, Ross, Resnick, Sidney
We consider a random censoring model for survival analysis, allowing the possibility that only a proportion of individuals in the population are susceptible to death or failure, and the remainder are immune or cured. Susceptibles suffer the event under study eventually, but the time at which this occurs may not be observed due to censoring. Immune individuals have infinite lifetimes which are always
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Pandemic-type failures in multivariate Brownian risk models Extremes (IF 1.3) Pub Date : 2021-09-22 Dȩbicki, Krzysztof, Hashorva, Enkelejd, Kriukov, Nikolai
Modelling of multiple simultaneous failures in insurance, finance and other areas of applied probability is important especially from the point of view of pandemic-type events. A benchmark limiting model for the analysis of multiple failures is the classical d-dimensional Brownian risk model (Brm), see Delsing et al. (Methodol. Comput. Appl. Probab. 22(3), 927–948 2020). From both theoretical and practical
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Choquet random sup-measures with aggregations Extremes (IF 1.3) Pub Date : 2021-09-20 Wang, Yizao
A variation of Choquet random sup-measures is introduced. These random sup-measures are shown to arise as the scaling limits of empirical random sup-measures of a general aggregated model. Because of the aggregations, the finite-dimensional distributions of introduced random sup-measures do not necessarily have classical extreme-value distributions. Examples include the recently introduced stable-regenerative
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Branching processes with immigration in atypical random environment Extremes (IF 1.3) Pub Date : 2021-09-20 Foss, Sergey, Korshunov, Dmitry, Palmowski, Zbigniew
Motivated by a seminal paper of Kesten et al. (Ann. Probab., 3(1), 1–31, 1975) we consider a branching process with a conditional geometric offspring distribution with i.i.d. random environmental parameters An, n ≥ 1 and with one immigrant in each generation. In contrast to above mentioned paper we assume that the environment is long-tailed, that is that the distribution F of \(\xi _{n}:=\log ((1-A_{n})/A_{n})\)
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Extremes of subexponential Lévy-driven random fields in the Gumbel domain of attraction Extremes (IF 1.3) Pub Date : 2021-09-11 Stehr, Mads, Rønn-Nielsen, Anders
We consider a spatial Lévy-driven moving average with an underlying Lévy measure having a subexponential right tail, which is also in the maximum domain of attraction of the Gumbel distribution. Assuming that the left tail is not heavier than the right tail, and that the integration kernel satisfies certain regularity conditions, we show that the supremum of the field over any bounded set has a right
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The tail process and tail measure of continuous time regularly varying stochastic processes Extremes (IF 1.3) Pub Date : 2021-06-05 Philippe Soulier
The goal of this paper is to investigate the tools of extreme value theory originally introduced for discrete time stationary stochastic processes (time series), namely the tail process and the tail measure, in the framework of continuous time stochastic processes with paths in the space \(\mathcal {D}\) of càdlàg functions indexed by \(\mathbb {R}\), endowed with Skorohod’s J1 topology. We prove that
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Truncated pair-wise likelihood for the Brown-Resnick process with applications to maximum temperature data Extremes (IF 1.3) Pub Date : 2021-06-01 Zhendong Huang, Olga Shulyarenko, Davide Ferrari
Max-stable processes are a natural extension of multivariate extreme value theory important for modeling the spatial dependence of environmental extremes. Inference for max-stable processes observed at several spatial locations is challenging due to the intractability of the full likelihood function. Composite likelihood methods avoid these difficulties by combining a number of low-dimensional likelihood