Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Diagnostics for dependence within time series extremes.
AU - Tawn, Jonathan A.
AU - Ledford, Anthony W.
N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research
PY - 2003/5
Y1 - 2003/5
N2 - 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.
AB - 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.
U2 - 10.1111/1467-9868.00400
DO - 10.1111/1467-9868.00400
M3 - Journal article
VL - 65
SP - 521
EP - 543
JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)
JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)
SN - 1369-7412
IS - 2
ER -