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## CURRENTLY DISPLAYING:

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## Maxima of moving maxima of continuous functions

### Extremes (2012-09-01) 15: 267-297 , September 01, 2012

Maxima of moving maxima of continuous functions (CM3) are max-stable processes aimed at modelling extremes of continuous phenomena over time. They are defined as Smith and Weissman’s M4 processes with continuous functions rather than vectors. After standardization of the margins of the observed process into unit-Fréchet, CM3 processes can model the remaining spatio-temporal dependence structure. CM3 processes have the property of joint regular variation. The spectral processes from this class admit particularly simple expressions given here. Furthermore, depending on the speed with which the parameter functions tend toward zero, CM3 processes fulfill the finite-cluster condition and the strong mixing condition. Processes enjoying these three properties also enjoy a simple expression for their extremal index. Next a method to fit CM3 processes to data is investigated. The first step is to estimate the length of the temporal dependence. Then, by selecting a suitable number of blocks of extremes of this length, clustering algorithms are used to estimate the total number of different profiles. The parameter functions themselves are estimated thanks to the output of the partitioning algorithms. The full procedure only requires one parameter which is the range of variation allowed among the different profiles. The dissimilarity between the original CM3 and the estimated version is evaluated by means of the Hausdorff distance between the graphs of the parameter functions.

## Excursion probabilities of isotropic and locally isotropic Gaussian random fields on manifolds

### Extremes (2016-09-29): 1-13 , September 29, 2016

Let *X* = {*X*(*p*), *p* ∈ *M*} be a centered Gaussian random field, where *M* is a smooth Riemannian manifold. For a suitable compact subset
$D\subset M$
, we obtain approximations to the excursion probabilities
$\mathbb {P}\{\sup _{p\in D} X(p) \ge u \}$
, as
$u\to \infty $
, for two cases: (i) *X* is smooth and isotropic; (ii) *X* is non-smooth and locally isotropic. For case (i), the expected Euler characteristic approximation is formulated explicitly; while for case (ii), it is shown that the asymptotics is similar to Pickands’ approximation on Euclidean space which involves Pickands’ constant and the volume of *D*. These extend the results in Cheng and Xiao (Bernoulli *22*, 1113–1130 2016) from spheres to general Riemannian manifolds.

## Distribution of the height of local maxima of Gaussian random fields

### Extremes (2015-06-01) 18: 213-240 , June 01, 2015

Let {*f*(*t*) : *t* ∈ *T*} be a smooth Gaussian random field over a parameter space *T*, where *T* may be a subset of Euclidean space or, more generally, a Riemannian manifold. We provide a general formula for the distribution of the height of a local maximum ℙ{*f*(*t*_{0}) > *u*|*t*_{0} is a local maximum of *f*(*t*)} when *f* is non-stationary. Moreover, we establish asymptotic approximations for the overshoot distribution of a local maximum ℙ{*f*(*t*_{0}) > *u*+*v*|*t*_{0} is a local maximum of *f*(*t*) and *f*(*t*_{0}) > *v*} as
$v\to \infty $
. Assuming further that *f* is isotropic, we apply techniques from random matrix theory related to the Gaussian orthogonal ensemble to compute such conditional probabilities explicitly when *T* is Euclidean or a sphere of arbitrary dimension. Such calculations are motivated by the statistical problem of detecting peaks in the presence of smooth Gaussian noise.

## Polar decomposition of regularly varying time series in star-shaped metric spaces

### Extremes (2017-02-24): 1-28 , February 24, 2017

There exist two ways of defining regular variation of a time series in a star-shaped metric space: either by the distributions of finite stretches of the series or by viewing the whole series as a single random element in a sequence space. The two definitions are shown to be equivalent. The introduction of a norm-like function, called *modulus*, yields a polar decomposition similar to the one in Euclidean spaces. The angular component of the time series, called *angular* or *spectral tail process*, captures all aspects of extremal dependence. The stationarity of the underlying series induces a transformation formula of the spectral tail process under time shifts.

## Exact simulation of Brown-Resnick random fields at a finite number of locations

### Extremes (2015-06-01) 18: 301-314 , June 01, 2015

We propose an exact simulation method for Brown-Resnick random fields, building on new representations for these stationary max-stable fields. The main idea is to apply suitable changes of measure.

## On tail trend detection: modeling relative risk

### Extremes (2015-06-01) 18: 141-178 , June 01, 2015

The climate change dispute is about changes over time of environmental characteristics (such as rainfall). Some people say that a possible change is not so much in the mean but rather in the extreme phenomena (that is, the average rainfall may not change much but heavy storms may become more or less frequent). The paper studies changes over time in the probability that some high threshold is exceeded. The model is such that the threshold does not need to be specified, the results hold for any high threshold. For simplicity a certain linear trend is studied depending on one real parameter. Estimation and testing procedures (is there a trend?) are developed. Simulation results are presented. The method is applied to trends in heavy rainfall at 18 gauging stations across Germany and The Netherlands. A tentative conclusion is that the trend seems to depend on whether or not a station is close to the sea.

## A weak law of large numbers for maxima

### Extremes (2011-09-01) 14: 325-341 , September 01, 2011

A weak law of large numbers related to the classical Gnedenko results for maxima (see Gnedenko, Ann Math 44:423–453, 1943) is established.

## Extreme value analysis for the sample autocovariance matrices of heavy-tailed multivariate time series

### Extremes (2016-09-01) 19: 517-547 , September 01, 2016

We provide some asymptotic theory for the largest eigenvalues of a sample covariance matrix of a *p*-dimensional time series where the dimension *p* = *p*_{n} converges to infinity when the sample size *n* increases. We give a short overview of the literature on the topic both in the light- and heavy-tailed cases when the data have finite (infinite) fourth moment, respectively. Our main focus is on the heavy-tailed case. In this case, one has a theory for the point process of the normalized eigenvalues of the sample covariance matrix in the iid case but also when rows and columns of the data are linearly dependent. We provide limit results for the weak convergence of these point processes to Poisson or cluster Poisson processes. Based on this convergence we can also derive the limit laws of various function als of the ordered eigenvalues such as the joint convergence of a finite number of the largest order statistics, the joint limit law of the largest eigenvalue and the trace, limit laws for successive ratios of ordered eigenvalues, etc. We also develop some limit theory for the singular values of the sample autocovariance matrices and their sums of squares. The theory is illustrated for simulated data and for the components of the S&P 500 stock index.

## Multivariate extremes and the aggregation of dependent risks: examples and counter-examples

### Extremes (2009-06-01) 12: 107-127 , June 01, 2009

Properties of risk measures for extreme risks have become an important topic of research. In the present paper we discuss sub- and superadditivity of quantile based risk measures and show how multivariate extreme value theory yields the ideal modeling environment. Numerous examples and counter-examples highlight the applicability of the main results obtained.

## Extremal dependence measure and extremogram: the regularly varying case

### Extremes (2012-06-01) 15: 231-256 , June 01, 2012

The dependence of large values in a stochastic process is an important topic in risk, insurance and finance. The idea of risk contagion is based on the idea of large value dependence. The Gaussian copula notoriously fails to capture this phenomenon. Two notions in a process or vector context which summarize extremal dependence in a function comparable to a correlation function are the *extremal dependence measure* (EDM) and the *extremogram*. We review these ideas and compare the two tools and end with a central limit theorem for a natural estimator of the EDM which allows drawing confidence bands comparable to those provided by Bartlett’s formula in a classical context of sample correlation functions.