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Diagnostics for dependence within time series extremes.

Research output: Contribution to journalJournal article

Published

Journal publication date05/2003
JournalJournal of the Royal Statistical Society: Series B (Statistical Methodology)
Journal number2
Volume65
Number of pages23
Pages521-543
Original languageEnglish

Abstract

Summary. The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence. We present a range of new diagnostic tools for assessing whether these assumptions are appropriate and for identifying structure within extreme events. These tools are based on tail characteristics of joint survivor functions but can be implemented by using existing estimation methods for extremes of univariate independent and identically distributed variables. Our diagnostic aids are illustrated through theoretical examples, simulation studies and by application to rainfall and exchange rate data. On the basis of these diagnostics we can explain characteristics that are found in the observed extreme events of these series and also gain insight into the properties of events that are more extreme than those observed.

Bibliographic note

RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research