Long-range dependence

Résumé

Long-range dependence (LRD), also called long memory or long-range persistence, is a phenomenon that may arise in the analysis of spatial or time series data. It relates to the rate of decay of statistical dependence of two points with increasing time interval or spatial distance between the points. A phenomenon is usually considered to have long-range dependence if the dependence decays more slowly than an exponential decay, typically a power-like decay. LRD is often related to self-similar processes or fields. LRD has been used in various fields such as internet traffic modelling, econometrics, hydrology, linguistics and the earth sciences. Different mathematical definitions of LRD are used for different contexts and purposes. Short-range dependence versus long-range dependence One way of characterising long-range and short-range dependent stationary process is in terms of their autocovariance functions. For a short-range dependent process, the coupling between values at d

Source officielle

https://en.wikipedia.org/wiki/Long-range_dependence

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (7)

Publications associées

Chargement

Personnes associées (1)

Personnes associées

Chargement

Unités associées (1)

Unités associées

Chargement

Concepts associés (3)

Concepts associés

Chargement

Cours associés

Chargement

Séances de cours associées (5)

Séances de cours associées

Chargement

Extreme events are responsible for huge material damage and are costly in terms of their human and economic impacts. They strike all facets of modern society, such as physical infrastructure and insurance companies through environmental hazards, banking and finance through stock market crises, and the internet and communication systems through network and server overloads. It is thus of increasing importance to accurately assess the risk of extreme events in order to mitigate them. Extreme value theory is a statistical approach to extrapolation of probabilities beyond the range of the data, which provides a robust framework to learn from an often small number of recorded extreme events. In this thesis, we consider a conditional approach to modelling extreme values that is more flexible than standard models for simultaneously extreme events. We explore the subasymptotic properties of this conditional approach and prove that in specific situations its finite-sample behaviour can differ significantly from its limit characterisation. For modelling extremes in time series with short-range dependence, the standard peaks-over-threshold method relies on a pre-processing step that retains only a subset of observations exceeding a high threshold and can result in badly-biased estimates. This method focuses on the marginal distribution of the extremes and does not estimate temporal extremal dependence. We propose a new methodology to model time series extremes using Bayesian semiparametrics and allowing estimation of functionals of clusters of extremes. We apply our methodology to model river flow data in England and improve flood risk assessment by explicitly describing extremal dependence in time, using information from all exceedances of a high threshold. We develop two new bivariate models which are based on the conditional tail approach, and use all observations having at least one extreme component in our inference procedure, thus extracting more information from the data than existing approaches. We compare the efficiency of these models in a simulation study and discuss generalisations to higher-dimensional setups. Existing models for extremes of Markov chains generally rely on a strong assumption of asymptotic dependence at all lags and separately consider marginal and joint features. We introduce a more flexible model and show how Bayesian semiparametrics can provide a suitable framework allowing simultaneous inference for the margins and the extremal dependence structure, yielding efficient risk estimates and a reliable assessment of uncertainty.

EPFL2018

Chargement

Bayesian Semiparametrics for Modelling the Clustering of Extreme Values

Thomas Lugrin

Extreme events can be statistically characterised as excesses of a high threshold. Inference in this case has to account for dependence between excesses. The peaks over threshold approach suggests pre-processing the series by defining clusters of successive observations and making inference only on maxima of those clusters, which can be assumed independent. There exists however no good method for specifying the clusters, causing quantiles for long return periods to be potentially badly biased. The peaks over threshold method uses asymptotic results as approximation at finite levels; our goal is to focus on the subasymptotic model suggested by Eastoe and Tawn (2012) to develop new Bayesian semiparametric techniques to properly approach the problem, thus getting an estimation of the full excess distribution uncertainty. A simulation study shows the instability in estimated quantiles based on the peaks over threshold method compared to the subasymptotic model across different cluster definitions. An application on river peakflows is also presented and shows how the subasymptotic model, fitted with the novel semiparametric Bayesian method, can be applied to real data. This work is divided into three main parts: an introductory part which provides an insight into multivariate extremes, with a particular attention to special kinds of dependence involved in this framework. In the second part we introduce a conditional multivariate model and develop a semiparametric Gibbs sampler to fit this particular model. The last part deals with the subasymptotic approach, meant to model short-range dependence of excesses over a threshold. We discuss further improvements and alternatives in the last section.

2013

Chargement

Bayesian Uncertainty Management in Temporal Dependence of Extremes

Anthony Christopher Davison, Thomas Lugrin, Jonathan A. Tawn

Both marginal and dependence features must be described when modelling the extremes of a stationary time series. There are standard approaches to marginal modelling, but long- and short-range dependence of extremes may both appear. In applications, an assumption of long-range independence often seems reasonable, but short-range dependence, i.e., the clustering of extremes, needs attention. The extremal index $0

Springer Verlag2016

Chargement

EPFL2018